Rodent model and related device, compositions, methods and systems

ABSTRACT

Provided herein are methods and systems and related composition, to provide a rodent model having a target microbiome profile formed by a target presence, a target proportion and/or a target total load of a target prokaryote of a target taxon, based on absolute quantification of the target prokaryote. Further provided are rodents obtained by the methods herein described and related use in testing methods performed in connection with physiological or pathological conditions in an individual preferably a human individual. Also provided herein is a tailcup device and related use to prevent coprophagia in rodents.

CROSS-REFERENCE TO RELATED APPLICATION

The present application is a continuation-in-part of and claims priorityto U.S. application Ser. No. 16/927,496 entitled “AbsoluteQuantification of Nucleic Acids and Related Methods and Systems” filedon Jul. 13, 2020 with docket number P2506-US and to PCT application S/NPCT/US2020/041787 entitled “Absolute Quantification of Nucleic Acids andRelated Methods and Systems” filed on Jul. 13, 2020 with docket numberP2506-PCT, each of which in turn further claim priority to U.S.Provisional Application No. 62/961,584, entitled “A Method For AbsoluteQuantification of Nucleic Acids” filed on Jan. 15, 2020, with docketnumber CIT 8311-P2, to U.S. Provisional Application No. 62/873,838,entitled “A Method For Absolute Quantification of Nucleic Acids” filedon Jul. 12, 2019 with the docket number CIT 8311-P, to U.S. ProvisionalApplication No. 62/873,410, entitled “A method for developing a morehumanized rodent model” filed on Jul. 12, 2019 with the docket numberCIT 8310-P, and to the above mentioned U.S. Provisional Application No.62/960,527, entitled “A method for developing a more humanized rodentmodel” filed on Jan. 13, 2020 with the docket number CIT 8310-P2, thecontents of each of which are also incorporated by reference in itsentirety. The present application further claims priority to U.S.Provisional Application No. 62/960,527, entitled “A method fordeveloping a more humanized rodent model” filed on Jan. 13, 2020 withthe docket number CIT 8310-P2 and to U.S. Provisional Application No.62/961,584, entitled “A Method For Absolute Quantification of NucleicAcids” filed on Jan. 15, 2020, with docket number CIT 8311-P2, thecontents of each of which are incorporated by reference in its entirety.

STATEMENT OF GOVERNMENT GRANT

This invention was made with government support under Grant No.W911NF-17-1-0402 awarded by the US Army and Grant No. EFMA1137089awarded by the National Science Foundation. The government has certainrights in the invention.

FIELD

The present disclosure relates generally to rodent models and relateduse. In particular, the present disclosure relates to a rodent model andrelated devices, composition methods and systems.

BACKGROUND

Rodent models are used extensively in microbiome research and enable thespatial, temporal, compositional, and functional interrogation of thegastrointestinal microbiota and its effects on the host physiology anddisease phenotype.

Challenges however remain for developing rodent models having amicrobiome closely mimicking microbiomes of interest and in particularhuman gut microbiome when associated to physiological or pathologicalconditions of interest, which can provide more reliable model toinvestigate the causal roles of human microbiomes on the host physiologyand disease predisposition and phenotype, as well as drugspharmacokinetic and personalized medicine.

SUMMARY

Provided herein are customized rodent models and related devices,compositions methods and systems which in several embodiments allowproduction of customized rodent models having a gut microbiome withcontrolled presence, proportion and/or total load of target prokaryotes

According to a first aspect, a method is described to provide a rodentmodel having a target microbiome profile formed by a target presence, atarget proportion and/or a target total load of a target prokaryote of atarget taxon, the target taxon having a taxonomic rank lower than asample taxon in a same taxonomic hierarchy, the method comprising

providing a rodent having a rodent microbiome;

obtaining a sample of the rodent comprising the rodent microbiome toprovide a rodent sample;

quantifying absolute abundance of the target prokaryotes in the rodentsample, to obtain a rodent detected presence, a rodent detectedproportion and/or a rodent detected total load of the target prokaryotesin the rodent, each target prokaryote being of a target taxon having ataxonomic rank lower than a sample taxon in a same taxonomic hierarchy;and

comparing the rodent detected presence, the rodent detected proportionof and/or the rodent detected total load of the target prokaryotes withthe target presence, the target proportion and/or the target total loadof the target prokaryotes in the target microbiome profile.

In the method to provide a rodent model herein described, thequantifying absolute abundance of the target prokaryotes comprises

amplifying a 16S rRNA recognition segment comprising a 16S rRNA variableregion specific for the target taxon flanked by target 16S rRNAconserved regions specific for the sample taxon, by performingamplification of nucleic acids extracted from the sample with primerscomprising a primer target sequence specific for the target 16S rRNAconserved regions to quantitatively detect an absolute abundance ofprokaryotes of the sample taxon in the sample and to provide anamplified 16S rRNA recognition segment,

sequencing the amplified 16S rRNA recognition segment with primerscomprising the primer target sequence specific for the target 16S rRNAconserved region and the 16S rRNA variable regions to detect a relativeabundance of the prokaryotes of the target taxon with respect to theprokaryotes of the sample taxon in the sample, and

multiplying the relative abundance of the prokaryotes of the targettaxon in the sample times absolute abundance of the prokaryotes of thesample taxon in the sample to quantify the absolute abundance of theprokaryotes of the target taxon in the sample.

In the method to provide a rodent model herein described, the detectedpresence of the target prokaryote in the rodent is provided by thedetected relative abundance and/or absolute abundance wherein presenceis detected when the relative abundance is greater than a threshold.In the method to provide a rodent model herein described, the detectedproportion is provided by the relative abundance of the targetprokaryote and/or by the quantified absolute abundance in combinationwith the detected relative abundance providing the detected proportionof the target prokaryote in the rodent.In the method to provide a rodent model herein described the total loadof the target prokaryote is provided by detected absolute abundance ofthe target prokaryote of the target taxa and/or by the detected absoluteabundance of prokaryotes of the sample taxon.

The method to provide a rodent model according to the disclosure canoptionally further comprise

modifying the rodent microbiome by introducing, enriching and/ordepleting prokaryotes in the rodent microbiome to provide the rodentmicrobiome with the target prokaryotes with the target presence, thetarget proportion, and/or the target total load;

obtaining a sample of the rodent comprising the rodent microbiomefollowing the modifying to obtain a rodent modified sample;

quantifying absolute abundance of the target prokaryotes in the rodentmodified sample, to obtain a detected rodent modified presence, adetected rodent modified proportion and/or a detected rodent modifiedtotal load of the target prokaryotes in the rodent, each targetprokaryote being of a target taxon having a taxonomic rank lower than asample taxon in a same taxonomic hierarchy; and

comparing the detected rodent modified presence, the detected rodentmodified proportion and/or the detected rodent modified total load ofthe target prokaryotes with the target presence, the target proportionand/or the target total load of the target prokaryotes in the targetmicrobiome profile.

The method to provide a rodent model according to the disclosure canoptionally further comprise

repeating the modifying, the obtaining to provide a rodent modifiedsample and the quantifying absolute abundance of the target prokaryotesin the rodent modified sample until the detected rodent modifiedpresence, the detected rodent modified proportion and/or the detectedrodent modified total load of the target prokaryote is substantially thesame of the target presence, the target proportion and/or the targettotal load of the target prokaryotes, to obtain the rodent model havingthe target microbiome profile. In preferred embodiments, the microbiomeis a microbiome of the gastrointestinal tract of an individual, and themodifying comprises or consist of preventing coprophagia of the rodent.

According to a second aspect, a system is described to provide a rodentmodel having a target gut microbiome profile formed by a targetpresence, a target proportion and/or a target total load of a targetprokaryote of a target taxon. The system comprises primers comprisingthe target primer sequence specific for the target 16S rRNA conservedregions specific for the sample taxon, reagents to perform polymerasechain reaction, and reagents to perform amplicon sequencing forsimultaneous combined or sequential use to detect an absolute abundanceof target prokaryotes of the target taxon in the sample according to themethod herein described. The system can further comprise means forpreventing coprophagia preferably a tail cup according to the presentdisclosure.

According to a third aspect, a customized rodent model is describedhaving a target gut microbiome profile formed by a target presence, atarget proportion and/or a target total load of a target prokaryote of atarget taxon, and obtained by methods herein described to provide acustomized rodent model.

According to a fourth aspect methods and systems are described toperform testing of effects of a compound on physiological and/orpathological conditions associated with a target microbiome, having atarget microbiome profile, the method comprises providing a customizedrodent model according to the present disclosure having the targetmicrobiome profile, and performing the testing on the customized rodentmodel.

The system comprises the customized rodent model in combination with thecompound and/or reagents to perform the testing for simultaneouscombined or sequential use in the method to perform the testing hereindescribed.

According to a fifth aspect methods and systems are described to performtesting of effects of a target microbiome having a target microbiomeprofile on physiological and/or pathological conditions associated witha target microbiome, having a target microbiome profile, the methodcomprises providing a customized rodent model according to the presentdisclosure having the target microbiome profile, and performing thetesting on the customized rodent model.

The system comprises the customized rodent model in combination withreagents to perform the testing for simultaneous combined or sequentialuse in the method to perform the testing herein described.

According to a sixth aspect, a tail cup device is described for animalswith tails, the tail cup device comprising: a tubular-shaped cup fortrapping excreted feces of an animal, the tubular-shaped cup having aproximal surface configured to fit around a posterior end of the animaland a distal surface and a tail sleeve configured to cover a portion ofa tail of the animal and engageable with the tubular-shaped cup throughthe distal surface of the tubular-shaped cup, for mounting of thetubular-shaped cup at a tail base of the animal.

In the tail cup device, the distal surface of the tubular-shaped cupcomprises an orifice operating as a locking opening of thetubular-shaped cup to allow passing through of the tail sleeve from aninside to an outside of the tubular-shaped cup, the orifice having adiameter smaller than a diameter of the tail sleeve in order to allow alocking engagement of the tail sleeve with the tubular-shaped cup whenapplied to the tail of the animal.The tail cup device further comprises an unlocking slit for apressure-based opening of the tubular-shaped cup to engage and disengagethe tail sleeve to and from the tubular-shaped cup.

Customized rodent models, and related devices, compositions methods andsystems herein described, allow in several embodiments, a more accuratetesting of compounds and related properties in connection with targetphysiological and/or pathological conditions which are associated to atarget gut microbiome, such as various gastrointestinal disorders suchas SIBO, Crohn's disease, Ulcerative Colitis, Colon cancer, primarysclerosing cholangitis, ulcers, and Celiac disease.

Customized rodent models, and related devices, compositions methods andsystems herein described, allow in several embodiments a more accuratetesting of compounds and related properties following administrationthrough an individual gastrointestinal tract, such as testing ofpharmacokinetic of a drug, toxicology studies, drug interaction studiesand additional testing identifiable by a skilled person.

Customized rodent models, and related devices, compositions methods andsystems herein described, allow in several embodiments a more accuratetesting of the role of microbiome in connection with targetphysiological and/or pathological conditions such as cancer, autoimmunediseases, obesity, neurological diseases and additional conditionsidentifiable by a skilled person.

Customized rodent models, and related devices, compositions methods andsystems herein described, allow in several embodiments to prepareindividualized model mimicking the target microbiome of specificindividuals, and therefore allowing a more accurate testing inparticular when performed in connection with personalized medicine.

The customized rodent models and related devices methods and systemsherein described can be used in connection with various applicationswherein a model with a customized gut microbiome is desired. Forexample, the customized rodent models and related devices methods andsystems herein described can be used for quantitative microbiomeprofiling in human and animal microbiome research, research andinvestigation in various areas human and veterinary medicine, such asImmuno-Oncology, Immunology, Infectious Disease, Inflammation, MetabolicDisease, Neuroscience, Oncology and additional areas identifiable by askilled, drug research in particular when focused on drug effectivenessand pharmacokinetics, personalized medicine and additional applicationsidentifiable by a skilled person.

The details of one or more embodiments of the disclosure are set forthin the accompanying drawings and the description below. Other features,objects, and advantages will be apparent from the description anddrawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated into and constitute apart of this specification, illustrate one or more embodiments of thepresent disclosure and, together with the detailed description andexample sections, serve to explain the principles and implementations ofthe disclosure. Exemplary embodiments of the present disclosure willbecome more fully understood from the detailed description and theaccompanying drawings, wherein:

FIG. 1 shows a schematic illustration of the single-step 16S rRNA geneDNA quantification and amplicon barcoding workflow (BC-qPCR)implementation for quantitative microbiome profiling. (FIG. 1 Panel A)Sample collection and DNA extraction. (FIG. 1 Panel B) BC-qPCR reactionsare prepared in replicates for more accurate quantification and uniformamplicon barcoding. (FIG. 1 Panel C) Amplification and barcoding of theV4 region of microbial 16S rRNA gene are performed under real-timefluorescence measurements on a real-time PCR instrument. Pr-F—forwardprimer, Pr-R—reverse primer, IA-P5 and IA-P7—Illumina adapters P5 and P7respectively, BC—barcode. (FIG. 1 Panel D) Quantitative PCR data (Cqvalues) are recorded. Mock data are shown for illustration. (FIG. 1Panel E) Barcoded samples are quantified, pooled, purified, andsequenced on an NGS instrument. (FIG. 1 Panel F) NGS sequencing resultsprovide data on relative abundances of microbial taxa (mock chart datawere constructed only for illustrative purposes). Microbial taxarelative abundance profiles are converted to microbial absolute orabsolute fold-difference abundance profiles using the absolute orabsolute fold-difference data (16S rRNA gene DNA loads) measured in thecorresponding samples in step (D) (FIG. 1 Panel D) (mock chart data wereconstructed only for illustrative purposes).

FIG. 2 shows schematic drawings describing anchoring approaches forderiving the absolute abundances or absolute abundance fold differencesimplemented with the single-step 16S rRNA gene DNA quantification andamplicon barcoding workflow (BC-qPCR). (FIG. 2 Panel A) Anchoring with asingle standard and assumed BC-qPCR efficiency. (FIG. 2 Panel B)Anchoring with two or more standards and calculated batch-specificBC-qPCR efficiency. (FIG. 2 Panel C) Estimation of the absolute folddifference among samples with unknown total 16S rRNA gene DNA copy loadin the absence of standards.

FIG. 3 shows an exemplary optimization of the protocol for microbial 16SrRNA gene DNA copy quantification in samples without and with highmammalian DNA background. (FIG. 3 Panel A) Sequence alignment of theoriginal EMP and modified forward primers targeting the V4 region ofmicrobial 16S rRNA gene are shown with the E. coli 16S rRNA gene andmouse and human mitochondrial 12S rRNA gene sequences (SEQ ID NO: 1 toSEQ ID NO: 5). (FIG. 3 Panel B) Amplification products of the complexmicrobiota DNA sample containing 100 ng/μL of GF mouse DNA obtained withthe original EMP or modified forward primers. (FIG. 3 Panel C)Performance of the quantitative PCR reaction with the modifiednon-barcoded primers performed on serial 10-fold dilutions of thecomplex microbiota DNA sample with and without 100 ng/μL of mouse DNA.(FIG. 3 Panel D) Improvement of the 16S rRNA gene DNA copy ddPCRquantification assay performance in the presence of 100 ng/μL of mouseDNA background as a result of the supplementation of intercalating dyeto the commercial droplet digital PCR (ddPCR) master mix.

FIG. 4 shows in some embodiments the optimization of the single-stepprotocol for microbial 16S rRNA gene DNA copy quantification andamplicon barcoding in samples without and with high mammalian DNAbackground. (FIG. 4 Panel A) Amplification products of the complexmicrobiota DNA sample containing 100 ng/μL of GF mouse DNA with thebarcoded original EMP (UN00F0+UN00R0) and barcoded modified(UN00F2+UN00R0) primer sets. (FIG. 4 Panel B) Barcoding quantitative PCRreaction performance with the serial 10-fold dilutions of the complexmicrobiota DNA sample (SPF mouse fecal microbiota) with and without 100ng/μL of mouse DNA. (FIG. 4 Panel C) Correlation of the BC-qPCR Cqvalues (Y-axis) with the absolute 16S rRNA gene DNA copy numbers(X-axis) previously determined in the same set of samples with andwithout high host DNA background (data in panel C are taken from [1, 2])using the UN00F2+UN00R0 qPCR assay.

FIG. 5 shows the absolute fold differences in the abundances of taxa(16S rRNA gene copies) in mouse mid-small intestine mucosal and lumenalsamples yielded by the BC-qPCR assay according to an exemplary method ofthe disclosure. NGS data obtained from [1, 2] were used to calculate thefold difference values among samples using the single-stepfold-difference approach (this disclosure) for each individual taxon(order level). Multiple comparisons between the four experimental groupsof mice were performed for each taxon using the Kruskal-Wallis test.

FIG. 6 shows in an embodiment quantitative DNA recovery using commercialextraction and purification kit (ZymoBIOMICS) from samples containingfecal microbial cells in the range of concentrations evaluated with aqPCR assay. “Fecal microbiota (w/o HRC step)”-serial 10-fold dilutionsof mouse fecal microbial suspension extracted with the kit with the HRCpurification step omitted (N=1 extraction per dilution, N=3 PCRreplicates). “Fecal microbiota (w/HRC step)”—serial 10-fold dilutions ofmouse fecal microbial suspension extracted with the kit and purifiedfrom PCR inhibitors using the “HRC” columns included with the kit (N=1extraction per dilution, N=3 PCR replicates). “Fecal DNA”—serialdilutions of the single extracted DNA sample from the undiluted mousefecal microbial suspension extracted with the kit according to themanufacturer's protocol (N=1 sample per dilution, N=3 PCR replicates).“NTC”—no-template control (N=4 PCR replicates).

FIG. 7 illustrates in three hypothetical scenarios the value of absolute(compared with relative) quantification. In this hypothetical, two taxa(Taxon A and Taxon B) are found in equal abundance (50:50) in a“healthy” state but in an 80:20 ratio in the “disease” state. Threepossible scenarios arise: (FIG. 7 Panel a) Taxon A increases inabundance while Taxon B remains the same; (FIG. 7 Panel b) Taxon Aremains unchanged while Taxon B decreases in abundance, and (FIG. 7Panel c) Taxon A and Taxon B both decrease, but Taxon B decreases by agreater magnitude.

FIG. 8 shows in some embodiments the lower limits of quantification fortotal microbial DNA extraction and 16S rRNA gene amplicon sequencing.(FIG. 8 Panel a) A comparison of theoretical and measured copies of the16S rRNA gene with digital PCR using an eight-member microbial communityspiked at a range of dilutions into germ-free (GF) mouse tissue fromsmall-intestine (SI) mucosa, cecum, and stool. Each bar plot shows asingle technical replicate for each matrix. (FIG. 8 Panel b) Relativeabundance of the eight taxa as predicted and measured after 16S rRNAgene amplicon sequencing. (FIG. 8 Panel c) Correlation between the mean(n=4) relative abundance of each taxon and the coefficient of variation(% CV) using a cecum sample from a mouse on a chow diet with an initialtemplate input of either 1.2×10⁷ or 1.2×10⁴ 16S rRNA gene copies. Eachanalysis comprised four technical (sequencing) replicates. Taxa foundonly in the low-input sample were labeled contaminants (markers with anx); taxa found in the high-input sample but not low input sample werelabeled dropouts (marker with a plus sign). Shading indicates thePoisson sampling 95% confidence interval (10,000 bootstrappedreplicates) at a sequencing read depth of 28,000. (FIG. 8 Panel d)Relationship between relative abundance threshold (see text for details)and sequencing read depths at 30%, 40%, and 50% CV thresholds.

FIG. 9 shows exemplary embodiments of using digital PCR (dPCR) anchoringof 16S rRNA gene amplicon sequencing to provide microbial absoluteabundance measurements. Taxon-specific dPCR demonstrates low biases inabundance measurements calculated by 16S rRNA gene sequencing with dPCRanchoring. (FIG. 9 Panel a) Correlation between the Logo abundance offour bacterial taxa as determined by taxa-specific dPCR and 16S rRNAgene sequencing with dPCR anchoring (relative abundance of a specifictaxon measured by sequencing * total 16S rRNA gene copies measured bydPCR). (FIG. 9 Panel b) The Log₂ ratio of the absolute abundance of fourbacterial taxa as determined either by taxa-specific dPCR or by 16S rRNAgene sequencing with dPCR anchoring (N=32 samples). Data points areoverlaid on the box and whisker plot. The body of the box plot goes fromthe first to third quartiles of the distribution and the center line isat the median. The whiskers extend from the quartiles to the minimum andmaximum data points within the 1.5× interquartile range, with outliersbeyond. All dPCR measurements are single replicates. (FIG. 9 Panel c)Analysis of beta diversity in cecum samples at a series of 1× dilutions(n=1 for each dilution). Mean Aitchison distance for six pairwisecomparisons of n=4 sequencing replicates of the undiluted (10⁸ copies)sample is shown for reference (error bar is standard deviation).Individual data points are overlaid on the replicates bar plot.

FIG. 10 demonstrates that microbial absolute abundances provideseparation between GI locations of mice on ketogenic or control diets.Analysis of data comparing ketogenic and control diets provides changesof total microbial loads, separation of microbial communities by GIlocation and by diet in principal component analysis, and the top taxadriving the separation of samples along the principal components. (FIG.10 Panel a) Overview of experimental setup and sample-collectionprotocol. Gastrointestinal tract (GIT) samples were collected from thefollowing regions: stomach, upper small intestine (SI), lower SI, cecum,colon, and stool. (FIG. 10 Panel b) Comparison of total microbial loadsbetween ketogenic and control diets in lumenal (top) and mucosal(bottom) samples collected after 10 days on each diet. The body of thebox plot goes from the first to third quartiles of the distribution andthe center line is at the median. The whiskers extend from the quartilesto the minimum and maximum data point within 1.5× interquartile range,with outliers beyond. (FIG. 10 Panel c) Principal component analysis(PCA) on the centered log-ratio transformed absolute abundances ofmicrobial taxa shows separation by GI location and diet (Ketogenic,circles and triangles; Control, X's and crosses). (FIG. 10 Panel d)Ranked order of the eigenvector coefficients scaled by the square rootof the corresponding eigenvalue (feature loadings) for the top twoprincipal components. The two most positive and most negative taxa areshown.

FIG. 11 demonstrates that analyses of relative and absolute microbialabundances from the same dataset result in different conclusions. (FIG.11 Panel a) PCA on centered log-ratio transformed relative abundancedata and log transformed absolute-abundance data (only the vectors ofthe five features with the largest magnitude are shown). (FIG. 11 Panelb) The impact of each taxon in the principal-component space, with twotaxa indicated to illustrate the comparison. (FIG. 11 Panel c) Acomparison of the taxa determined to be significantly different betweendiets using relative versus absolute quantification (N=6 mice per diet).P-values were determined by Kruskal-Wallis. Each point represents asingle taxon; dark greypoints indicate taxa with the absolute value ofP-value ratios greater than 2.5; red points indicate two taxa thatdisagreed significantly between the relative and absolute analyses.(FIG. 11 Panel d) For illustrative purposes, a comparison ofAkkermansia(g) relative abundance (percentage of Akkermansia), absoluteabundance (Akkermansia load), and total microbial load between stoolsamples from one mouse on each diet (Ketogenic, light-grey; Control,dark-grey). Whitebars indicate loads prior to the diet switch when allmice were on the chow diet.

FIG. 12 demonstrates that incorporating quantification limits enhancesdifferential taxon analysis as shown in stool and SI mucosa. Aquantitative framework that explicitly incorporates limits ofquantification separates differential microbial taxa into four classes,and for each GI location identifies a distinct set of differential taxa,including taxa with opposite patterns in stool and SI mucosa. (a-b)Microbial taxa in stool (FIG. 12 Panel a) or lower small-intestine (FIG.12 Panel b) mucosa in mice on ketogenic (N=6) and control (N=6) diets.The fold change on the x-axis is the Log₂ ratio of the average absoluteloads of taxon loads in each diet. Negative values indicate lower loadsin ketogenic diet compared to control diet. The q-value for a taxonindicates the significance of the difference in absolute abundancesbetween the two diets and were obtained by Kruskal-Wallis with aBenjamini-Hochberg correction for multiple hypothesis testing. The Log₁₀absolute abundance of each taxon is indicated by circle size. The dashedline is shown at a q-value representing a 10% false-discovery rate.(c-d) A subset of taxa from stool (FIG. 12 Panel c) and lower SI mucosa(FIG. 12 Panel d) that were significantly different between diets(q-values <0.1) and their corresponding fold change, absolute abundance(larger of the average absolute abundances between the two diets), andquantification class. Quantification class is determined by whether oneor both measurements were above or below the lower limit ofquantification and the limit of detection.

FIG. 13 shows two plots illustrating the total DNA loads in smallintestine and large intestine mucosa and lumen. Extracted DNA samplesfrom mice in the ketogenic-diet group were measured by Nanodrop (totalDNA) and digital PCR (microbial DNA). The horizontal lines represent themeans and the points represent individual biological replicates (N=24for small intestine; N=12 for large intestine).

FIG. 14 shows a plot illustrating the extraction and total DNAmeasurement accuracy of an eight-member mock microbial communitydilution spiked into extraction buffer or small-intestine mucosa, cecum,or stool from germ free mice. Log₂ fold change between theoretical anddPCR measured copies of 16S rRNA gene after extraction with varyinginput levels. Three technical replicates for buffer extractions areshown. All other sample types shown are N=1 to illustrate the biologicalnoise among sample types.

FIG. 15 shows the chimeric sequence prevalence as a function of thenumber of PCR cycles. The plot demonstrates that the chimeric sequenceprevalence is not determined by the number of PCR cycles. Relationshipbetween the number of PCR cycles during the amplification reaction forlibrary prep and the percentage of chimeric sequences detected byDivisive Amplicon Denoising Algorithm 2 (DADA2) [3]. N=33 samples thatwere sequenced from mice in the ketogenic-diet group.

FIG. 16 shows the Poisson limits of sequencing accuracy. (FIG. 16 Panela) Relationship between the relative abundance of each taxon and %coefficient of variation (CV) using four technical (sequencing)replicates of a mouse cecum sample with an initial template input of1.2×10⁴ 16S rRNA gene copies. The red shading indicates the bootstrapped(B=104) Poisson sampling confidence interval of the input 16S rRNA genecopies. (FIG. 16 Panel b) Bootstrapped Poisson sampling relationshipbetween % CV and percentage abundance as a function of read depth.

FIG. 17 shows in some embodiments the optimization of group-specificprimers to eliminate amplification of host DNA. Relative abundance ofnon-specific product amplified from 20 ng/μL small-intestine mucosasample from a germ-free mouse measured by qPCR. Lower Cq values indicatemore amplification. Each color represents a different annealingtemperature used during the cycling process. Samples were run in singletat each temperature.

FIG. 18 demonstrates the impact of ordination method on datavisualization. (FIG. 18 Panel a) Principal coordinates analysis (PCoA)plot using Bray-Curtis dissimilarity metric of all samples collected 10days after the diet switch. (FIG. 18 Panel b) Principal componentanalysis (PCA) plot using log-transform of absolute abundance data afteradding a pseudocount of 1 read to all taxa.

FIG. 19 shows the comparison of relative and absolute abundancequantification of Akkermansia(g) between mice on ketogenic and controldiet. Average Akkermansia(g) load from stool of N=6 mice on control diet(dark grey) and N=6 mice on ketogenic diet (light-grey). White pointsand bars indicate loads prior to the diet switch when all mice were onthe chow diet. Data points from mice without Akkermansia(g) are notshown. Bar plots show mean plus or minus the standard deviation.Individual data points are overlaid on the bar plots.

FIG. 20 demonstrates in two plots that absolute-abundance measurementsenable unbiased determination of correlation structure in microbiomedatasets. Correlation matrices, using Spearman's rank, for the totalmicrobial load and the top 30 most abundant taxa in stool samples frommice on either a ketogenic diet (FIG. 20—Panel a) or control diet (FIG.20 Panel b). The color of each marker is based on the correlationcoefficient (darker indicates higher correlation coefficients) and thesize is determined by the q-value of the correlation afterBenjamini-Hochberg multiple testing correction. False-discovery rates(FDR) indicate the q-value at which the correlation was deemedsignificant: 1%, 5%, 10%. Abbreviations: (f), family; (g), genus; (o),order.

FIG. 21 shows a plot demonstrating that the uncertainty in taxonabsolute-abundance measures approximately follows a normal distribution.The quantile-quantile (Q-Q) plot of the mean-centered log₂ relativeerror of absolute taxon abundances. The relative error is calculated asthe ratio of the absolute taxon loads measured by our method ofquantitative sequencing with dPCR anchoring over the absolute loadsmeasured by taxon-specific primers in dPCR (data are from FIG. 9, panelb). The x-axis represents the theoretical quantiles from a normaldistribution while the y-axis is the actual quantiles of themean-centered log₂ relative errors.

FIG. 22 shows a table listing the contaminant taxa with greater than 1%abundance in negative-control extraction.

FIG. 23 shows a table comparing digital PCR anchoring method forabsolute abundance measurements and other published absolute abundancemethods [4-7].

FIG. 24 shows a table with composition of ketogenic and control dietsused in this study based on previously reported diets (Envigo,Indianapolis, Ind., USA) [8].

FIG. 25 shows a table listing the absolute abundance, relativeabundance, fold change and quantification class for each differentiallyabundant taxon in the stool 10 days after diet switch.

FIG. 26 shows a table listing the absolute abundance, relativeabundance, fold change, and quantification class for each differentiallyabundant taxon in the lower small-intestine mucosa 10 days after dietswitch.

FIG. 27 shows a table listing the primers used in this study, relevantconditions, and specificity. All primers (SEQ ID NO: 6-15) were testedin silico for coverage of their desired taxonomic group and specificity[1, 2, 9-13].

FIG. 28 shows an overview of the study design and timeline. (FIG. 28Panel A) Mice from two age cohorts (4-months-old and 8-months-old) wereraised co-housed (four mice to a cage) for 2-6 months. One mouse fromeach cage was then assigned to one of the four experimental conditions:(functional tail cups (TC-F), mock tail cups (TC-M), housing on wirefloors (WF), and controls housed in standard conditions (CTRL). All micewere singly housed and maintained on each treatment for 12-20 days(N=24, 6 mice per group). (FIG. 28 Panel B) Samples were taken from sixsites throughout the gastrointestinal tract. Each sample was analyzed byquantitative 16S rRNA gene amplicon sequencing of lumenal contents (CNT)and mucosa (MUC) and/or quantitative bile-acid analyses of CNT. FIG. 28Panel B is adapted from [14, 15]).

FIG. 29 shows the quantification of microbial loads in lumenal contentsand mucosa of the gastrointestinal tracts (GIT) of mice in the fourexperimental conditions: (functional tail cups (TC-F), mock tail cups(TC-M), housing on wire floors (WF), and controls housed in standardconditions (CTRL). (FIG. 29 Panel A) Total 16S rRNA gene DNA copy loads,a proxy for total microbial loads, were measured along the GIT of miceof all groups (STM=stomach; SI1=upper third of the small intestine (SI),SI2=middle third or the SI, SI3=lower third of the SI roughlycorresponding to the duodenum, jejunum, and ileum respectively;CEC=cecum; COL=colon). Multiple comparisons were performed using aKruskal-Wallis test, followed by pairwise comparisons using theWilcoxon-Mann-Whitney test with false-discovery rate (FDR) correction.Individual data points are overlaid onto box-and-whisker plots; whiskersextend from the quartiles (Q2 and Q3) to the last data point within 1.5×interquartile range (IQR). (FIG. 29 Panel B) Correlation between themicrobial loads in the lumenal contents (per g total contents) and inthe mucosa (per 100 ng of mucosal DNA) of the mid-SI. N=6 mice perexperimental group.

FIG. 30 shows the compositional and quantitative 16S rRNA gene ampliconsequencing of the gut microbiota. (FIG. 30 Panel A) Principal componentsanalysis (PCA) of the log₁₀-transformed and standardized (mean=0,S.D.=1) absolute microbial abundance profiles in the stomach, mid-smallintestine, and cecum. Loadings of the top contributing taxa are shownfor each principal component. (FIG. 30 Panel B) Mean relative andabsolute abundance profiles of microbiota in the mid-SI (order-level)for all experimental conditions. Functional tail cups (TC-F), mock tailcups (TC-M), housing on wire floors (WF), and controls housed instandard conditions (CTRL). N=6 mice per experimental group, 4 of whichwere used for sequencing. (FIG. 30 Panel C) Absolute abundances ofmicrobial taxa (order-level) compared between coprophagic andnon-coprophagic mice along the mouse GIT. *Chloroplast and*Richettsiales (mitochondria) represent 16S rRNA gene DNA amplicons fromfood components of plant origin. Multiple comparisons were performedusing the Kruskal-Wallis test.

FIG. 31 shows the inference of microbial genes involved in bile-acid andxenobiotic conjugate modification along the GIT of coprophagic andnon-coprophagic mice. Inferred absolute abundance of the microbial genesencoding (FIG. 31 Panel A) bile salt hydrolases (cholylglycinehydrolases), (FIG. 31 Panel B) beta-glucuronidases, and (FIG. 31 PanelC) arylsulfatases throughout the GIT (STM=stomach; SI2=middle third ofthe small intestine (SI) roughly corresponding to the jejunum;CEC=cecum). KEGG orthology numbers are given in parentheses for eachenzyme. In all plots, individual data points are overlaid ontobox-and-whisker plots; whiskers extend from the quartiles (Q2 and Q3) tothe last data point within 1.5× interquartile range (IQR). Multiplecomparisons were performed using the Kruskal-Wallis test; pairwisecomparisons were performed using the Wilcoxon-Mann-Whitney test with FDRcorrection. N=4 mice per group.

FIG. 32 shows the bile acid profiles in gallbladder bile and in lumenalcontents along the entire GIT. (FIG. 32 Panel A) Total bile acid levels(conjugated and unconjugated; primary and secondary) and (FIG. 32 PanelB) the fraction of unconjugated bile acids in gallbladder bile andthroughout the GIT (STM=stomach; SI1=upper third of the small intestine(SI), SI2=middle third or the SI, SI3=lower third of the SI roughlycorresponding to the duodenum, jejunum, and ileum respectively;CEC=cecum; COL=colon). In all plots, individual data points are overlaidonto box-and-whisker plots; whiskers extend from the quartiles (Q2 andQ3) to the last data point within 1.5× interquartile range (IQR).Multiple comparisons were performed using the Kruskal-Wallis test;pairwise comparisons were performed using the Wilcoxon-Mann-Whitney testwith FDR correction. N=6 mice per group.

FIG. 33 shows the tail cup design and experimental setup for preventingcoprophagy. (FIG. 33 Panels A, B, C) Functional (TC-F, left) and mock(TC-M, right) tail cups as viewed from different perspectives. (FIG. 33Panel D) The standard cages with wire mesh floors used in this study(WF). (FIG. 33 Panels E, F) Ventral view of the functional (TC-F; left)and mock (TC-M, right) tail cups 24 hours after emptying (TC-F) or mockemptying (TC-M).

FIG. 34 shows the mounting of functional tail cups onto mice. (FIG. 34Panels A, B) Ventral and dorsal view of the tail sleeve mounted at thetail base. (FIG. 34 Panels C, D) Ventral and dorsal view of thefunctional tail cup installed and locked in place using the tail sleeve.

FIG. 35 shows a plot of body weight changing across all groups of micein relation to food intake over the course of the study. (FIG. 35 PanelA) Body weights of each individual animal at the beginning and at theendpoint of the study. (FIG. 35 Panel B) Normalized food intake per gramof body weight per day measured over the entire duration of the study.Multiple comparisons of the normally-distributed homoscedastic data wereperformed using one-way ANOVA; pairwise comparisons were performed usingthe Student's t-test with FDR correction. N=6 mice per group.

FIG. 36 shows the quantification of the culturable microbial load andmicrobiota profile along the entire GIT of mice fitted with functionaltail cups (TC-F) and control mice (CTRL). (FIG. 36 Panel A) Culturablemicrobial loads in contents along the gastrointestinal tract wereevaluated using the most probable number (MPN) assay performed inanaerobic BHI-S broth (N=5 mice per group, P-values were calculatedusing the Wilcoxon-Mann-Whitney test). (FIG. 36 Panel B) PCA analysis ofthe CLR-transformed relative microbial abundance profiles (16S rRNA geneamplicon sequencing) along the entire GIT in TC and CT mice (N=1 mousefrom each group).

FIG. 37 shows the bile acid profiles in gallbladder bile and in lumenalcontents along the entire GIT. (FIG. 37 Panel A) Total secondary bileacid levels (conjugated and unconjugated) and (FIG. 37 Panel B) thefraction of secondary bile acids (conjugated+unconjugated) ingallbladder bile and throughout the GIT (STM=stomach; SI1=upper third ofthe small intestine (SI), SI2=middle third or the SI, SI3=lower third ofthe SI roughly corresponding to the duodenum, jejunum, and ileumrespectively; CEC=cecum; COL=colon). In all plots, individual datapoints are overlaid onto box-and-whisker plots; whiskers extend from thequartiles (Q2 and Q3) to the last data point within 1.5× interquartilerange (IQR). Multiple comparisons were performed using theKruskal-Wallis test; pairwise comparisons were performed using theWilcoxon-Mann-Whitney test with FDR correction. N=6 mice per group.

FIG. 38 shows a table listing the primer oligonucleotide sequences (SEQID NO: 16-24) used in the study. [NNNNNNNNNNNN]— 12-base barcodesequences “806rcbc” (SEQ ID NO: 25) according to [16]. Additionaldescription of the primers is provided in the following referencesUN00F2 [17], UN00R0 [16, 18], UN00F2_BC [17], UN00R0_BC [16, 18],ILM00F(P5) [16, 18-21], ILM00R(P7), Seq_UN00F2_Read_1 [17],Seq_UN00R0_Read_2 [16, 18], Seq_UN00R0_RC_Index [16, 18].

FIG. 39 shows a table listing thermocycling parameters for thequantitative PCR (qPCR) assay for 16S rRNA gene DNA copy quantification.

FIG. 40 shows a table listing the thermocycling parameters for thedigital PCR (dPCR) assay for absolute 16S rRNA gene DNA copyquantification.

FIG. 41 shows a table listing thethermocycling parameters for the 16SrRNA gene DNA amplicon barcoding PCR reaction for next generationsequencing (NGS).

FIG. 42 shows a table listing the thermocycling parameters for thedigital PCR (dPCR) assay for barcoded amplicon and Illumina NGS libraryquantification.

FIG. 43 shows a table listing the reagents and chemical standards usedin the bile acid metabolomics assay.

FIG. 44 shows the cytokine levels in blood plasma cross all four groupsof animals. The cytokine levels did not demonstrate robustgroup-dependency. INFg, IL-1b, IL-2, IL-4, IL-6, IL-10, and IL-12p70 inthe majority of animals were present at the levels below the lower limitof quantification (LLOQ) (black horizontal line) of the assay. A trendtowards higher IL-6 in both groups of mice fitted with either functionalor mock tail cups could suggest the stress-related increase of thiscytokine.

FIGS. 45 to 48 show a schematic representation of a tail-cup device inaccordance with some embodiments of the present disclosure.

FIG. 49 illustrates in an exemplary embodiment the microbial loaddistribution across 250 human duodenal aspirate samples. (FIG. 49 PanelA) Histogram of the total microbial load in 250 duodenal aspiratesamples overlaid with a kernel-density estimate. (FIG. 49 Panel B)Quantile-quantile plot comparing the sample distribution of thelog₁₀-transformed total microbial load in duodenal aspirate samples to anormal distribution. (FIG. 49 Panel C) Kernel-density estimate plotsshowing the absolute abundance distribution for the taxa with greaterthan 50% prevalence in duodenal aspirates. Prevalence (defined as ataxon's frequency of occurrence in our dataset) and number of sampleswith each genus are labeled next to the distribution. A legend indicatesstrict anaerobes (gray line through 02) and the location each genus iscommonly found (saliva and/or stool)[22, 23].

FIG. 50 illustrates in an embodiment the total microbial load breakdownby age (FIG. 50 Panel A) and gender (FIG. 50 Panel B).

FIG. 51 illustrates in an embodiment the distribution of total microbialload from subpopulations of patients: taking probiotics (N=49), activesmokers (N=16), taking antibiotics in the past 6 months (N=100), ortaking proton pump inhibitors (PPI, N=106).

FIG. 52 illustrates in an embodiment the comparison of the culturecounts from aerobic (MacConkey agar) and anaerobic (blood agar) platesto the total load of microbes expected to grow on these plates. (FIG. 52Panel A) Scatterplot comparing aerobic culture load from MacConkeyplates to total load from 16S quantitative sequencing of only the subsetof bacteria that are known to grow on MacConkey plates(Escherichia-Shigella, Enterobacteriaceae, Enterococcus, andAeromonas)[24]. (FIG. 52 Panel B) Scatterplot comparing anaerobicculture load, from blood agar plates, to total load from sequencing ofprevalent bacteria that are expected to grow on blood agar plates(Prevotella, Streptococcus, Fusobacterium, Escherichia-Shigella)[25].Gray dashed line indicates limit of detection of quantitative sequencingmethod. N=244. (Six patients in the study were lacking culture data.)

FIG. 53 illustrates in an exemplary embodiment the relationship betweensaliva and duodenal aspirate microbiomes. (FIG. 53 Panel A) Totalmicrobial load of 21 paired duodenal aspirate and saliva samples. (FIG.53 Panel B) No significant correlation between the total microbial loadof 21 paired duodenal aspirate and saliva samples. (FIG. 53 Panel C)Percentage of taxa in duodenal aspirate samples also present in paired(same patient) vs randomly paired saliva samples. (FIG. 53 Panel D)Volcano plot showing the ratio of relative abundances of species induodenum vs saliva samples. The dashed line indicates a significancethreshold at q=0.1 (Kruskal-Wallis with Benjamini-Hochberg correction).

FIG. 54 illustrates in an exemplary embodiment the taxa co-correlations.In particular, the co-correlations reveal which taxa co-occur in highabundance and which can be considered disruptor taxa. (FIG. 54 Panel A)Co-correlation matrix of the top 20 most abundant genera and totalmicrobial load. Only significant correlations (q<0.1, Benjamini-Hochbergcorrection) are shown. Size of each marker is determined by the Spearmancorrelation coefficient. The eight disruptor taxa are bolded. (FIG. 54Panel B) Clustered co-correlation matrix of the top 16 genera ranked bythe difference between their maximum abundance and mean abundance. Twocommon genera in the dataset are shown at the bottom for reference.Darker squares indicate a higher Spearman correlation coefficient.Disruptor taxa labels are bolded. Genera are labeled with knownrelevance to human health. HAI=hospital acquired infection,IBD=inflammatory bowel disease.

FIG. 55 illustrates in an exemplary embodiment the microbiota diversitycontrolled by strict anaerobes and disruptor taxa. (FIG. 55 Panel A) PCAplot of absolute microbial abundances at the genus level with the toptwo correlated metadata variables overlaid. (FIG. 55 Panel B) Featureloadings for principal component 2. Top five value-ranked genera in eachdirection (positive and negative) are in light grey and labeled. (FIG.55 Panel C) Correlation between the strict anaerobic microbial load andfacultative anaerobic microbial load. (FIG. 55 Panel D) Relationshipbetween the percentage abundance of strict anaerobes and Shannondiversity index.

FIG. 56 illustrates in an exemplary embodiment the domination ofdisruptor species in SIBO samples and their correlation with GI symptomsand the inflammatory cytokine IL8. (FIG. 56 Panel A) Principal componentanalysis (PCA) of absolute microbial abundances at the genus level.Colors indicate non-SIBO (grey) or SIBO (black) participants asdetermined by culture. “X” markers indicate samples from non-SIBOparticipants that contained Lactobacillus. The PC1 axis correlates withtotal load and the PC2 axis correlates with the abundance of disruptortaxa. (FIG. 56 Panel B) Volcano plot indicating the taxa that differedbetween SIBO and non-SIBO samples. The dashed line indicates thesignificance threshold at q=0.01. (FIG. 56 Panel C) Correlation betweenPC2 (disruptor axis) and patient-reported symptom scores (on a 0-100scale). The dashed line represents significance threshold at q=0.05.(FIG. 56 Panel D) Correlation between PC2 and patient serum cytokinelevels. The dashed lines represent the significance thresholds atq=0.05. (FIG. 56 Panel E) Boxplot indicating increasing average totalmicrobial load with increasing number of disruptor taxa with loadsgreater than 10⁴ rRNA gene copies/mL (not including Lactobacillus). Asignificant difference between total load in samples with zero disruptortaxa and total load in samples with at least 1 disruptor taxa wasobserved (P<0.001). (FIG. 56 Panel F) Percentage of samples frompatients with either 0 symptoms or 5-6 symptoms (out of 6 categories)for individuals with varying loads of disruptor taxa (not includingLactobacillus).

FIG. 57 shows in an embodiment the IL8 levels in samples with andwithout Clostridium perfringens.

FIG. 58 shows in an embodiment an empiric cumulative distributionfunction (ECDF) plot demonstrating the enrichment of Enterobacteriaceae(N=33), Escherichia-Shigella (N=24), and Campylobacter (N=59) in highload samples vs Lactobacillus (N=42) and the common taxa Prevotella(N=104).

FIG. 59 shows in an embodiment a plot demonstrating the relationshipbetween Lactobacillus load and bloating symptoms in samples containingadditional (non-Lactobacillus) disruptor taxa.

FIG. 60 shows in an embodiment the violin plots with data pointsoverlaid for patient-reported symptom scores. Binary threshold fordetermining whether severe symptoms exist was set at the median scorereported of each symptom, shown by the dashed lines.

FIG. 61 illustrates in an embodiment the disruptor taxa load separatedby patient age: 18-39 (N=40), 40-49 (N=31), 50-59 (N=58), 60-69 (N=67),70-83 (N=54).

FIG. 62 shows in an embodiment the relationship between absoluteabundance (greater than 10⁵ copies/mL) and relative abundance ofdisruptor loads (Spearman, P=0.09, not significant)

DETAILED DESCRIPTION

Provided herein are a customized rodent model and related methods andsystems for providing a rodent with customized microbiome through asabsolute quantification of a corresponding prokaryote in the microbiome.

The term “microbial” “microbe” or “microorganism”, as used hereinindicates a microscopic living organism, which can exist in asingle-celled form or in a colony of cells form. Microorganisms compriseextremely diverse unicellular organisms, including prokaryotes and inparticular bacteria, but also including fungi (yeast and molds), andprotozoal parasites as will be understood by a skilled person.

The term “prokaryote” is used herein interchangeably with the terms“cell” and refers to a microbial species which contains no nucleus orother membrane-bound organelles in the cell. Exemplary prokaryotic cellsinclude bacteria and archaea.

The term “bacteria” or “bacterial cell”, used herein interchangeablywith the term “cell” indicates a large domain of prokaryoticmicroorganisms. Typically a few micrometers in length, bacteria have anumber of shapes, ranging from spheres to rods and spirals, and arepresent in most habitats on Earth, such as terrestrial habitats likedeserts, tundra, Arctic and Antarctic deserts, forests, savannah,chaparral, shrublands, grasslands, mountains, plains, caves, islands,and the soil, detritus, and sediments present in said terrestrialhabitats; freshwater habitats such as streams, springs, rivers, lakes,ponds, ephemeral pools, marshes, salt marshes, bogs, peat bogs,underground rivers and lakes, geothermal hot springs, sub-glacial lakes,and wetlands; marine habitats such as ocean water, marine detritus andsediments, flotsam and insoluble particles, geothermal vents and reefs;man-made habitats such as sites of human habitation, human dwellings,man-made buildings and parts of human-made structures, plumbing systems,sewage systems, water towers, cooling towers, cooling systems,air-conditioning systems, water systems, farms, agricultural fields,ranchlands, livestock feedlots, hospitals, outpatient clinics,health-care facilities, operating rooms, hospital equipment, long-termcare facilities, nursing homes, hospice care, clinical laboratories,research laboratories, waste, landfills, radioactive waste; and the deepportions of Earth's crust, as well as in symbiotic and parasiticrelationships with plants, animals, fungi, algae, humans, livestock, andother macroscopic life forms. Bacteria in the sense of the disclosurerefers to several prokaryotic microbial species which compriseGram-negative bacteria, Gram-positive bacteria, Proteobacteria,Cyanobacteria, Spirochetes and related species, Planctomyces,Bacteroides, Flavobacteria, Chlamydia, Green sulfur bacteria, Greennon-sulfur bacteria including anaerobic phototrophs, Radioresistantmicrococci and related species, Thermotoga and Thermosipho thermophilesas would be understood by a skilled person. Taxonomic names of bacteriathat have been accepted as valid by the International Committee ofSystematic Bacteriology are published in the “Approved Lists ofBacterial Names” [26] as well as in issues of the International Journalof Systematic and Evolutionary Microbiology. More specifically, thewording “Gram positive bacteria” refers to cocci, nonsporulating rodsand sporulating rods that stain positive on Gram stain, such as, forexample, Actinomyces, Bacillus, Clostridium, Corynebacterium,Cutibacterium (previously Propionibacterium), Erysipelothrix,Lactobacillus, Listeria, Mycobacterium, Nocardia, Staphylococcus,Streptococcus, Enterococcus, Peptostreptococcus, and Streptomyces.Bacteria in the sense of the disclosure refers also to the specieswithin the genera Clostridium, Sarcina, Lachnospira, Peptostreptococcus,Peptoniphilus, Helcococcus, Eubacterium, Peptococcus, Acidaminococcus,Veillonella, Mycoplasma, Ureaplasma, Erysipelothrix, Holdemania,Bacillus, Amphibacillus, Exiguobacterium, Gracilibacillus, Halobacillus,Saccharococcus, Salibacillus, Virgibacillus, Planococcus, Kurthia,Caryophanon, Listeria, Brochothrix, Staphylococcus, Gemella,Macrococcus, Salinococcus, Sporolactobacillus, Marinococcus,Paenibacillus, Aneurinibacillus, Brevibacillus, Alicyclobacillus,Lactobacillus, Pediococus, Aerococcus, Abiotrophia, Dolosicoccus,Eremococcus, Facklamia, Globicatella, Ignavigranum, Carnobacterium,Alloiococcus, Dolosigranulum, Enterococcus, Melissococcus,Tetragenococcus, Vagococcus, Leuconostoc, Oenococcus, Weissella,Streptococcus, Lactococcus, Actinomyces, Arachnia, Actinobaculum,Arcanobacterium, Mobiluncus, Micrococcus, Arthrobacter, Kocuria,Nesterenkonia, Rothia, Stomatococcus, Brevibacterium, Cellulomonas,Oerskovia, Dermabacter, Brachybacterium, Dermatophilus, Dermacoccus,Kytococcus, Sanguibacter, Jonesia, Microbacteirum, Agrococcus,Agromyces, Aureobacterium, Cryobacterium, Corynebacterium, Dietzia,Gordonia, Skermania, Mycobacterium, Nocardia, Rhodococcus, Tsukamurella,Micromonospora, Propioniferax, Nocardioides, Streptomyces, Nocardiopsis,Thermomonospora, Actinomadura, Bifidobacterium, Gardnerella, Turicella,Chlamydia, Chlamydophila, Borrelia, Treponema, Serpulina, Leptospira,Bacteroides, Porphyromonas, Prevotella, Flavobacterium, Elizabethkingia,Bergeyella, Capnocytophaga, Chryseobacterium, Weeksella, Myroides,Tannerella, Sphingobacterium, Flexibacter, Fusobacterium,Streptobacillus, Wolbachia, Bradyrhizobium, Tropheryma, Megasphera,Anaeroglobus, Escherichia-Shigella, Klebsiella, muribaculum,alloprevotella, paraprevotella, oscillibacter, candidatus arthromitus,aeromonas, romboutsia, campylobacter, salmonella, faecalibacterium,roseburia, blautia, oribacterium, ruminococcus.

The term “Archaea” as used herein refers to prokaryotic microbialspecies of the division Mendosicutes, such as Crenarchaeota andEuryarchaeota, and include but is not limited to methanogens(prokaryotes that produce methane); extreme halophiles (prokaryotes thatlive at very high concentrations of salt (NaCl); extreme (hyper)thermophiles (prokaryotes that live in extremely hot environments),Methanobrevibacter, and methanosphaera.

Accordingly, the term “microbial community” as used herein refers to agroup of microorganisms sharing an environment which can comprise one ormore prokaryotes or individual genera or species of prokaryotes. Amicrobial community in the sense of the disclosure can thus include twoor more microorganisms two or more strains, two or more species. two ormore genera, two or more families, or any mixtures of microorganisms inthe sense of the disclosure with additional life form such as viruses,comprised in the shared environment. The interaction between the two ormore community members may take different forms and can be in particularcommensal, symbiotic and pathogenic as will be understood by a skilledperson. An exemplary microbial community is the ‘microbiome” of anindividual which is an aggregate of all microbiota (all microorganismsfound in and on all multicellular organisms) residing on or withintissues and biofluids of the individual.

The term “individual” as used herein indicates any multicellularorganism that can comprise microorganisms, thus providing a sharedenvironment for microbial communities, in any of their tissues, organs,and/or biofluids. Exemplary individual in the sense of the disclosureinclude plants, algae, animals, fungi, and in particular, vertebrates,mammals more particularly humans.

In particular, in individual having a digestive tract (e.g. allvertebrates and in particular humans, as well as most invertebratesincluding sponges, cnidarians, and ctenophores) the microbiome residingin or within the digesting tract, (generally comprising bacteria andarchaea), is also indicated as “gastrointestinal microbiome” or gutmicrobiome.

Additional fluids hosting a microbial community in individuals such asvertebrates and human comprise tear fluid, saliva, nasal, oral,tonsillar, and pharyngeal swabs, sputum, bronchoalveolar lavage (BAL),gastric, small-intestine, and large-intestine contents and aspirates,feces, bile, pancreatic juice, urine, vaginal samples, semen, skinswabs, tissue and tumor biopsy, blood, lymph, cerebrospinal fluid,amniotic fluid, mammary gland secretions/breast milk and tumor tissues.

Accordingly in a human individual, in addition to gastrointestinalmicrobiome, further microbiomes comprise eye microbiome, skinmicrobiome, mammary glands microbiome, placenta microbiome, seminalfluid microbiome, uterus microbiome, ovarian follicles microbiome, lungmicrobiome, saliva microbiome, oral mucosa microbiome, conjunctivamicrobiome, biliary tract microbiome, tumor microbiome and additionalmicrobiomes.

Additional exemplary microbiome in individuals comprise insectmicrobiome plant root microbiome (rhizosphere), aquaculture (fisheries,clam farms) and others identifiable to a person skilled in the art.

Microbial communities in the sense of the disclosure can also be foundin a target environment outside an individual and comprising a medium (asubstance, either solid or liquid) including components such asnutrients allowing growth of microbes in the sense of the disclosureExemplary target environments comprise soil for plant growth, water,sediment, oil well samples, bioreactors (e.g., complex/mixed probiotics)and additional environment identifiable by a skilled person. Exemplarymicrobiomes in a target environment include, ocean microbiome, livingspace microbiome, clean room microbiome, and others identifiable to aperson skilled in the art.

Microbial communities and in particular microbiome are characterized bythe prokaryote composition and in particular by microbiome features thatcharacterize and distinguish the microbiome providing a related profile.

The term “microbiome profile” as used herein indicates a combination offeatures that characterize a microbiome and can be used to identify anddistinguish the microbiome over different microbiome or the samemicrobiome under different conditions. Features that can be used toprovide a microbiome profile comprise, presence, proportion and totalload of prokaryotes comprised in the microbiome which are associatedwith and characterize the microbiome under certain conditions.Prokaryotes whose presence, proportions and/or total load can form amicrobiome profile under certain conditions, can be identified by askilled person.

For example, the microbiome profile in the sense of the disclosure of amicrobiome of a species, an organ and/or an individual with a diseasetypically comprise presence proportion and/or total load of a prokaryoteassociated with the species, organ, and/or diseases respectively.Exemplary prokaryote whose presence proportion or total load areassociated with a condition comprise gut pathogens such as Escherichia,Serratia, Salmonella, Clostridium difficile associated withgastrointestinal pathologies, Exemplary prokaryote whose presenceproportion or total load are associated with a microbiome of a speciescomprise, Clostridiales, Bacteroidales, Erysipelotrichale associatedwith the gut microbiome of a rodent in view of as a result ofself-inoculation (see Example 14) Exemplary prokaryote whose presenceproportion or total load are associated with an organ in the individualprobiotics such as Lactobacillus[27] associated with thegastrointestinal tract (Example 14).

In particular the presence of a prokaryote in the microbiome can bedetermined based on an absolute abundance of the prokaryote and/or of arelative abundance of the prokaryote in the microbiome. In particular,presence can be detected when the relative abundance and/or the absoluteabundance are above a certain threshold. In some embodiments thethreshold is arbitrarily, set or set in view of considerations relatedto experimental settings. In some embodiments, the threshold can bedetermined in view of statistical considerations to reduce or minimizeinaccurate detection (e.g. application of Poisson statistics), In someembodiments the threshold can be a low value number (e.g. 0.01% relativeabundance). In some embodiments the threshold can be zero.

In particular the proportion of a prokaryote in the microbiome can bedetermined based on relative abundance and/or by absolute abundance ofthe prokaryote in combination a relative abundance of the prokaryote inthe microbiome to provide a proportion value indicative of theproportion of the prokaryote with respect to one or more of additionalprokaryotes, typically all prokaryotes of the microbiome.

The total load of the prokaryotes can be determined based on an absoluteabundance of the prokaryote in the microbiome. In particular, the totalload of prokaryote can comprise one or more prokaryotes of interest, andtypically comprises all prokaryotes of a microbiome.

Prokaryotes that can be used to identify features of a microbiomeprofile can comprise one or more types of prokaryotes and canadvantageously identified with the absolute quantification method inaccordance with the present disclosure.

In methods and systems described herein and a rodent model is providedwhich has a rodent microbiome profile formed by a rodent presence, arodent proportion and/or a rodent total load of a rodent prokaryote of arodent taxon,

The term “rodent” as used herein, rodents indicates mammals of theRodentia order, which are characterized by a single pair of continuouslygrowing incisors in each of the upper and lower jaws as will beunderstood by a person skilled in the art. Examples of rodents includemice, rats, squirrels, prairie dogs, porcupines, beavers, guinea pigs,hamsters, gerbils and others identifiable to a person skilled in theart. Rodents commonly used for experimental purpses comprise mice, rats,guinea pigs, hamsters, and rabbits. Common commercially availablestrains are Mus musculus, Rattus norvegicus, Cricetus auratus, andOryctolagus cuniculus.

A “rodent model” in the sense of the disclosure is animal model whereinthe animal is a rodent. An animal model is a living, non-human speciesused in medical research and investigation of human disease because itcan mimic aspects of the disease found in humans. Animal models aretypically used to obtain information about a disease and its prevention,diagnosis, and treatment. By using animal models, researchers can carryout experiments that would be impractical or ethically prohibited withhumans. Animal models can be altered or genetically engineered to havean existing, inbred or induced disease or injury that is similar to ahuman condition. For example, experimental models are models of diseasethat resemble human conditions in phenotype or response to treatment butare induced artificially in the laboratory as will be understood by aperson skilled in the art.

Methods are herein described comprise providing a rodent which istypically selected in view of the relevant aspect of human physiologyand or disease to be modeled in the customized rodent model.

In embodiments herein described, the rodent selected has a rodentmicrobiome and a corresponding rodent microbiome profile formed by arodent prokaryote presence, a rodent prokaryote proportion and/or arodent prokaryote total load of the target prokaryote of the targettaxon. The term rodent prokaryote is used herein to indicate anyprokaryote whose presence, proportion and/or total load is associatedwith and characterize a microbiome of a rodent animal. Preferredmicrobiomes comprise the microbiomes of the gastrointestinal tract (GIT)or of any portion thereof, such as oral cavity, small intestine, colonstomach, duodenum, jejunum and additional portions identifiable by askilled person.

In some embodiments the rodent microbiome can be a naturally occurringmicrobiome. In some embodiments, the rodent microbiome can be modifiedthrough various treatments identifiable by a skilled person. Forexample, in some embodiments. germ free (gnotobiotic) mice can be usedor mice treated with a cocktail of antibiotics to have a microorganismfree background which can then be colonized by specific microbes [28].

In particular, the rodent microbiome is the microbiome of a model organselected to simulate a target microbiome. Common organs are those of thegastrointestinal tract: oral cavity, stomach, small intestine (duodenum,jejunum, ileum), colon or the entire GI tract. Additional model organsare identifiable by a skilled person.

In some embodiment, providing a rodent is performed by providing arodent further having a selected genetic background as will beunderstood by a skilled person.

In particular, in some embodiments, a rodent can be selected that have agenetic background emulating a particular human disease genotype.Several common examples are the IL10−/− mouse model for humancolitis[29], the ob/ob mouse for human obesity[30], the SAMP1/YitFcmouse for human ileitis[31], and the NOD mouse for non-obese diabetes inhumans[32] and additional genetic background emulating a particularhuman disease.

In some embodiments, a rodent can be selected that has a geneticbackground commonly used in research of human conditions and arecommercially available laboratory mouse strains. Several common examplesare C57BL/6, BALB/c, and Swiss Webster mice.

In some embodiments, a rodent can be selected with a genetic backgroundresulting in in a defective immune system, such as immunocompromisedmice[33]. Several common examples are nude mice that lack a thymus, SCIDmice that are T and B cell deficient, and RAG knockout mice alsodeficient in T and B cells.

In some embodiments, a rodent can be selected with any geneticbackground and additional induced disease. For example, the DSS-colitismodel can be used with any genetic background. Dextran sodium sulfate isadded to the drinking water and results in colitis that can simulatehuman colitis.[34]

In methods and systems herein described, the rodent microbiome ismodified to obtain a rodent with target microbiome associated with aphysiological and/or pathological condition to be modeled in theresulting customized rodent model.

The term “target microbiome” as used herein indicates a microbiome to besimulated in the customized rodent model of the present disclosure.Target microbiome have profile formed by presence, proportion, and/ortotal load of one or more target prokaryotes, which are prokaryoteassociated with the target microbiome to be simulated. Exemplary targetmicrobiome comprise, any microbiome of an individual preferably humanbeings, or microbiome of an environment such as soil microbiome ormarine microbiome In preferred embodiments a target microbiome can beone or more of microbiomes of an individual, most preferably humanbeing, associated with a physiological or a pathological condition ofthe individual.

In some embodiments, a target prokaryote can comprise a microorganismthat has been associated with some form of pathological condition. Forexample, the Proteobacteria phylum consists of many opportunisticpathogens that are linked or directly cause disease (Escherichia,Serratia, Campylobacter, Enterococcus, Klebsiella, Pseudomonas,Staphylococcus, Salmonella, Yersinia). Additional gut pathogens beyondthe Proteobacteria phylum can also be used like Clostridium difficile.

In some embodiments, a target prokaryote can comprise any prokaryotethat is known to cause a change in the immune system. For example, manyClostridium species are known to induce an immune response that has beenlinked to the effectiveness of cancer treatments[35].

In some embodiments, a target prokaryote can comprise any prokaryoteused as a probiotic or food additive. Common examples areLactobacillus[27] and Bifidobacterium[36] strains.

In preferred embodiments, target prokaryotes can be any prokaryoteassociated with a target physiological or pathological condition to besimulated, (e.g. a disease) including physiological or pathologicalcondition identified by one or more target phenotypic features (such asone or more specific symptoms of a disease). A target prokaryote can beidentified by comparing microbiomes of individuals with thatphysiological or pathological condition against individuals without thephysiological or pathological condition and finding prokaryotes thatdiffer between these two groups. For example, identification of targetprokaryote in gut microbiome was performed in human patients with andwithout Crohn's disease to find that Enterobacteriaceae,Pasteurellaceae, Fusobacteriaceae, and Neisseriaceae are enriched inCrohn's disease patients[37]. Accordingly, in exemplary embodiments,prokaryote for each of Enterobacteriaceae, Pasteurellaceae,Fusobacteriaceae, and Neisseriaceae taxa, and/or the related combinationcan be used as target prokaryotes for a target gut microbiome of a hmanindividual having Crohn's disease. Additional, target prokaryotes forhuman gut microbiome or other microbiome of human or other individualsselected to be target microbiome can be identified by a skilled personupon reading of the present disclosure.

In embodiments herein described presence or absence of one or moretarget prokaryotes, proportion of the of one or more target prokaryoteswith respect to one or more preferably all additional prokaryotes in thetarget microbiome and/or total load of the target prokaryote in thetarget microbiome provide features of the target microbiome which form aprofile of the target microbiome, as will be understood by a skilledperson upon reading of the present disclosure.

An exemplary presence of a target prokaryote of a target prokaryotewhich can be used alone or in combination with proportion of the same orother target prokaryote and/or total load of the same and/or othertarget prokaryote to provide a microbiome profile, is the presence ofbacteroides genus with respect to the target microbiome of the smallintestine of coprophagic mice (see e.g. Examples 20-26) showingbacteroides presence is not detected after prevention of coprophagyperformed with the preferred tailcups of the present disclosure.

An additional exemplary presence of a target prokaryote of a targetprokaryote which can be used alone or in combination with proportion ofthe same or other target prokaryote and/or total load of the same and/orother target prokaryote to provide a microbiome profile, is the presenceof Clostridium difficile with respect to a target gut microbiomeassociated with a Clostridium difficile infection.

An exemplary proportion of a target prokaryote of a target prokaryotewhich can be used alone or in combination with presence of the same orother target prokaryote and/or total load of the same and/or othertarget prokaryote to provide a microbiome profile, is provided by therelative abundance of any target prokaryote (of any taxonomic level)with respect to target gut microbiome of coprophagic animals (such asany rodents) and non-coprophagic animals (such as human beings). Forexample, the relative abundance of any target prokaryote is higher incoprophagic animal compared to non-coprophagic animals. In particular,according to a coprophagy dataset the relative abundance of theLactobacillus genus has been detected to approximately 98% after theutilization of tail cups while it detected to be approximately roughly15% without utilization of tail cups and related coprophagy prevention.

An additional exemplary proportion of a target prokaryote which can beused alone or in combination with presence of the same or other targetprokaryote and/or total load of the same and/or other target prokaryoteto provide a microbiome profile, is provided by the relative abundanceof the top 10 or preferably of the top 50 or more preferably of the top100 most abundant taxa of the target microbiome preferably incombination with a total load (see e.g. the human gut microbiome as atarget microbiome to be provided in the rodent.

An exemplary total load of a target prokaryote of a target prokaryotewhich can be used alone or in combination with presence of the same orother target prokaryote and/or proportion of the same and/or othertarget prokaryote to provide a microbiome profile, is provided by theabsolute abundance of total prokaryotes a target microbiome normalizedto some sample input mass in a target microbiome, such as smallintestine target microbiome of coprophagic animal (see Examples 20-26)showing that the coprophagy dataset the total load of prokaryotes wasroughly 1×10{circumflex over ( )}6 16S copies/g in the small intestineafter utilization of tail cups while it was roughly 1×10{circumflex over( )}9 16S copies/g without utilization of tail cups preventingcoprophagy).

An additional exemplary total load of a target prokaryote of a targetprokaryote which can be used alone or in combination with presence ofthe same or other target prokaryote and/or proportion of the same and/orother target prokaryote to provide a microbiome profile, is provided bythe total microbial load with respect to human small intestinemicrobiome in individual having Small Intestine Bacterial Overgrowth(SIBO). In particular in such target microbiome the total microbial loadis at least 2 orders of magnitude higher than those of human smallintestine of human individuals without SIBO. In such target microbiome Ahigh total microbial load of specific target prokaryotes provided bysubsets of taxa (Enterobacteriaceae) is associated with targetmicrobiome of human individuals with SIBO having worse gastrointestinalsymptoms.

In methods and systems to provide a customized rodent model hereindescribed, a sample from the rodent is obtained comprising the rodentmicrobiome. The obtained sample can comprise any sample herein describedproviding an environment for the rodent microbiome. Different regions ofthe body of the rodent will require different sample collectionmethodologies.

For example, if the environment is comprised in the first part of thesmall intestine (duodenum), a sample from this region is collected fromthe host with proper collection techniques identifiable by a skilledperson. Stool can be used as a sample comprising rodent microbiome ofthe lumen of the colon. Small intestinal aspirates can be used for thelumen of the small intestine. Stool can simply be collected upon passagefrom the body while small intestinal aspirates will require anendoscopic procedure where a catheter is placed down the throat or nasalcavity, through the stomach and into the duodenum before a suction tubeis pushed through the catheter and utilized to suck up a portion ofliquid from the desired region.

In some embodiments of the methods and system to provide a customizedrodent according to the disclosure, a collected sample can preferably beappropriately stored to limit DNase mediated degradation of DNA in thesample. Examples of adequate storage include immediate transport to afreezer, snap freezing with liquid nitrogen, or DNase inactivation viamixing with a chaotropic agent (e.g., guanidinium thiocyanate).

The methods and systems to provide a customized rodent model hereindescribed, further comprise quantifying in the obtained sample absoluteabundance the target prokaryotes to obtain a detected presence,proportion and/or total load of the target prokaryotes in the rodent,each target prokaryote being of a target taxon having a taxonomic ranklower than a sample taxon in a same taxonomic hierarchy.

In order to perform the quantifying portion of the collected sample cantypically be removed be removed from the collection tube, weighed, andsubjected to nucleic acid extraction and purification, wherein nucleicacids can be extracted/purified via a nucleic acid extraction kit.Examples of kits include Qiagen DNeasy Powersoil Pro, Zymobiomics DNAminiprep, and magmax microbiome ultra nucleic acid isolation.

In particular the method to provide a rodent model herein described thequantifying is performed by methods and systems for absolutequantification of the disclosure which can be performed to provide anabsolute quantification of a prokaryote within a microbial communitythrough absolute quantification of the related 16SrRNA.

The term “16S rRNA” indicates the 16S ribosomal ribonucleic acid ofcomponent of the ribosome 30S subunit of a prokaryote, or a DNA encodingtherefor (herein 16S rRNA gene). A 16S rRNA of a prokaryote can beidentified by its a sedimentation coefficient which, an index reflectingthe downward velocity of the macromolecule in the centrifugal field. 16SrRNA performs various functions in a prokaryote such as providingscaffolding for the immobilization of ribosomal proteins, binds theshine Dalgarno sequence of mRNAs, interacts with 23S to help integratetwo ribosome units (50S+30S). Accordingly, the 16S ribosomal RNA is anecessary for the synthesis of all prokaryotic proteins and is thereforecomprised in all prokaryotes as will be understood by a skilled person.

The 16S rRNA is highly prevalent and highly conserved (overall) across abroad diversity of prokaryotes/in view of its role in the physiology ofprokaryotes, 16S ribosomal RNA is the most conserved among prokaryotes.Accordingly, 16S rRNA is a key parameter in molecular classification andphylogenetic analysis of prokaryote possibly applied to theidentification of clinical bacteria, sequence analysis and relatedtherapeutic and/or diagnostic application. In particular classificationand grouping of prokaryotes can be performed based on a sequencesimilarity in the 16S rRNA varying among prokaryotes based on theirtaxonomical ranks.

The term “taxonomy” or “taxon” refers to a group of one or moremicrobial organisms that are classified into a group based on theircommon characteristics. Taxonomic hierarchy refers to a sequence ofcategories arranging various organisms into successive levels of thebiological classification either in a decreasing or increasing orderfrom domain to species or vice versa. Taxonomic rank is the relativelevel of a group of organisms (a taxon) in a taxonomic hierarchy.Examples of taxonomic ranks include strain, species, genus, family,order, class, phylum, kingdom, domain and others as will be understoodby a person skilled in the art. Species is the basic taxonomic group inmicrobial taxonomy. Groups of species are then collected into genus.Groups of genera are collected into family, families into order, ordersinto class, classes into phylum, phyla into kingdom, and kingdoms intodomain.

As a person skilled in the art will understand, each taxonomic level hasincreasing sequence similarity between individual members of the sametaxonomic level from domain down to species. Individual taxonomic groupsat a specific rank can be defined by the conservation of their 16S rRNAgene sequence.

Accordingly, 16S rRNA in the sense of the disclosure comprises conservedregions and variable regions. The conserved regions being conservedamong prokaryotes with different degree of conservation among differenttaxa based on their taxonomic rank. The variable regions are insteadspecific for specific taxa with different degree of specificity amongdifferent taxa based on their taxonomic rank, as will be understood by askilled person.

16S rRNA conserved and variable region of a target taxon having ataxonomic rank can be identified by comparing 16S rRNA sequences of thetarget taxon and 16S rRNA sequences for a reference taxon having ataxonomic rank higher than the taxonomic rank of the target taxon toprovide a 16S rRNA sequence comparison. Identification of 16S rRNAvariable regions of the target taxon can be performed by selectingregions of the 16S rRNA sequences having at least 70% homology among the16S rRNA sequences of the reference taxon. Identification of 16S rRNAvariable regions of the target taxon can be performed by selectingregions of the 16S rRNA sequences having less than 70% homology with the16S rRNA sequences of the reference taxon.

Preferably 16S rRNA sequences are all available 16S rRNA sequences ofthe target taxon (e.g. known and/or through detection of 16S rRNA inprokaryotes of the target taxon) and encompass the entire length of the16S rRNA. More preferably the 16S rRNA sequences are DNA sequences andthe homology is detected among 16S rRNA DNA sequences.

As used herein, “homology”, “sequence identity” or “identity” in thecontext of two or more nucleic acid or polypeptide sequences makesreference to the nucleotide bases or residues in the two or moresequences that are the same when aligned for maximum correspondence overa specified comparison window. When percentage of sequence identity orsimilarity is used in reference to proteins, it is recognized thatresidue positions which are not identical often differ by conservativeamino acid substitutions, where amino acid residues are substituted witha functionally equivalent residue of the amino acid residues withsimilar physiochemical properties and therefore do not change thefunctional properties of the molecule.

A person skilled in the art would understand that similarity betweenpolynucleotide sequences is typically measured by a process thatcomprises the steps of aligning the two sequences to form alignedsequences, then detecting the number of matched characters, i.e.characters similar or identical between the two aligned sequences, andcalculating the total number of matched characters divided by the totalnumber of aligned characters in each polypeptide or polynucleotidesequence, including gaps. The similarity result is expressed as apercentage of identity.

As used herein, “percentage of sequence identity” means the valuedetermined by comparing two or more optimally aligned sequences over acomparison window, wherein the portion of the polynucleotide sequence inthe comparison window may comprise additions or deletions (gaps) ascompared to the reference sequence (which does not comprise additions ordeletions) for optimal alignment of the two or more sequences. Thepercentage is calculated by determining the number of positions at whichthe identical nucleic acid base or amino acid residue occurs in bothsequences to yield the number of matched positions, dividing the numberof matched positions by the total number of positions in the window ofcomparison, and multiplying the result by 100 to yield the percentage ofsequence identity.

As used herein, “reference sequence” is a defined sequence used as abasis for sequence comparison. A reference sequence may be a subset orthe entirety of a specified sequence; for example, as a segment of afull-length protein or protein fragment. A reference sequence cancomprise, for example, a sequence identifiable a database such asGenBank and UniProt and others identifiable to those skilled in the art.

As understood by those skilled in the art, determination of percentidentity between any two sequences can be accomplished using amathematical algorithm. Non-limiting examples of such mathematicalalgorithms are the algorithm of Myers and Miller [38], the localhomology algorithm of Smith et al. [39]; the homology alignmentalgorithm of Needleman and Wunsch [40]; the search-for-similarity-methodof Pearson and Lipman [41]; the algorithm of Karlin and Altschul [42],modified as in Karlin and Altschul [43]. Computer implementations ofthese mathematical algorithms can be utilized for comparison ofsequences to determine sequence identity. Such implementations include,but are not limited to: CLUSTAL in the PC/Gene program (available fromIntelligenetics, Mountain View, Calif.); the ALIGN program (Version 2.0)and GAP, BESTFIT, BLAST, FASTA [42], and TFASTA in the WisconsinGenetics Software Package, Version 8 (available from Genetics ComputerGroup (GCG), Madison, Wis., USA). Alignments using these programs can beperformed using the default parameters.

Accordingly, the term “conserved” as used herein in connection withnucleic acid regions indicates regions with homology of at least 70%,more preferably at least 80%, more preferably at least 90%, and mostpreferably 95%, more preferably 98% or 100%.

Conversely, the term “variable” as used herein in connection withnucleic acid regions indicates regions with homology of less than 70%possibly lower than 50%, lower than 30% or lower than 20% as will beunderstood by a skilled person.

In 16S rRNA in the sense of the disclosure, the conserved regions andthe variable regions are comprised in a configuration where the variableregions are flanked by conserved regions. In particular in a 16S rRNAaccording to the disclosure can comprise multiple conserved regionssequences flanking and/or interspaced with nine hypervariable regions.In particular, the 16S rRNA is atypically about 1500 bp and comprisesV1-V9 ranging from about 30 to 100 base pairs long flanked andinterspaced by conserved regions. The variable regions are involved inthe secondary structure of the encoded small ribosomal subunit as willbe understood by a person skilled in the art.

In 16S rRNA in the sense of the disclosure the configuration ofconserved and variable regions can be perform by detecting variabilityfor each base position within aligned 16S rRNA sequences by detectingthe frequency of the most common nucleotide residue and determining afrequency distribution by calculating one minus the frequency of themost common nucleotide residue. The resulting frequency distribution canbe adjusted by taking the mean frequency within a 50-base slidingwindow, moving 1 base position at a time along the alignment. Peakscorrespond to the hypervariable regions. Methods on how to locate theconserved and variable regions in 16S rRNA gene DNA can be found in, forexample, reference [44].

In a 16S rRNA in accordance with the disclosure formed by a 16S rRNA the16S rRNA gene is typically a DNA polynucleotide naturally occurring in aprokaryote and comprising variable regions flanked by conserved regionsin the configuration of the encoded 16S rRNA ribonucleotide.Accordingly, typically the length of the 16S rRNA gene is about 1500 bp.Prokaryotic cells can contain 1-20 copies, often 5 to 10 copies, of 16SrRNA each, which impact the detection sensitivity when detection isdirected to detection of prokaryotes of set taxon based on detection of16S rRNA in accordance with the present disclosure. Tools for predicting16S gene copy number of prokaryotes and related databases can be foundin public domains including published tools such as PICRUSt [45, 46],CopyRighter [47], PAPRICA [48], rrnDB [49] and others identifiable to aperson skilled in the art.

In embodiments herein described, tools for predicting 16S rRNA gene copynumber and/or databases can be used to detect a number of cells of aprokaryote of a target taxon based on a detected absolute number of 16SrRNA gene copies for that taxon, in addition or in place of detection of16S rRNA gene as will be understood by a skilled person upon review ofthe present disclosure. In particular, it will be understood by askilled person detection of 16S rRNA gene allows for a more accuratequantitation when the number of 16S rRNA gene copies per genome of thetarget prokaryote is not known, or it is desired to account for avariation of numbers of cells for a single prokaryote depending on itsphysiological state or growth rate.

Accordingly a 16S rRNA and related gene comprises conserved regionshighly prevalent and highly conserved across a broad diversity ofprokaryotes (about >80% of known bacterial and >80% of known archaeal16S sequences) A 16S rRNA and related gene also comprises variableregions which can allow differentiation/identification among prokaryotemembers of a same taxonomic rank.

In some embodiments herein described, detection of a 16S rRNArecognition segment comprising conserved and variable regionsidentifying a target 16S rRNA, is performed to obtain an absolutequantification of the target 16S rRNA and/or a corresponding prokaryotetarget taxon within a microbial community in a sample.

The terms “detect” or “detection” as used herein indicates thedetermination of the existence, presence or fact of a target in alimited portion of space, including but not limited to a sample, areaction mixture, a molecular complex and a substrate. The “detect” or“detection” as used herein can comprise determination of chemical and/orbiological properties of the target, including but not limited toability to interact, and in particular bind, other compounds, ability toactivate another compound and additional properties identifiable by askilled person upon reading of the present disclosure. The detection canbe quantitative or qualitative. A detection is “qualitative” when itrefers, relates to, or involves identification of a quality or kind ofthe target or signal in terms of relative abundance to another target orsignal, which is not quantified, such as presence or absence. Adetection is “quantitative” when it refers, relates to, or involves themeasurement of quantity or amount of the target or signal (also referredas quantitation), which includes but is not limited to any analysisdesigned to determine the amounts or proportions of the target orsignal. A quantitative detection in the sense of the disclosurecomprises detection performed semi-quantitatively, above/below a certainamount of nucleic acid molecules as will be understood by a skilledperson and/or using semiquantitative real time isothermal amplificationmethods including real time loop-mediated isothermal amplification(LAMP) (see e.g. semi quantitative real-time PCR). For a given detectionmethod and a given nucleic acid input, the output of quantitative orsemiquantitative detection method that can be used to calculate a targetnucleic acid concentration value is a “concentration parameter”.

The wording “absolute quantification” as used herein in connection witha nucleic acid such as 16S rRNA indicates detecting absolute numbers ofcopies of the nucleic acid within a target environment such as a sample.Accordingly, absolute quantification of a 16S rRNA as used hereinindicates the total number of 16S rRNA ribonucleotide or 16S rRNA genewithin a target environment, herein also indicated as “absoluteabundance”. Absolute quantification of a nucleic acid can be provided bydirect detection of the nucleic acid (by a digital amplification methodsuch as digital PCR which directly detect absolute copy numbers of atarget nucleic acid) and/or based on a comparative quantification of thenucleic acid in combination with a standard measurement (herein also“anchor” and/or by detecting fold differences between sample (e.g. byreal-time/qPCR).

Absolute quantification of a nucleic acid can be provided using afluorescence or spectrophotometric based method (e.g., Nanodrop orQubit) which is considered to be proportional to the levels of thenucleic acid to be quantified. Absolute quantification of a nucleic acidcan be provided by cell counting based methods such as flow cytometry,optical density, plating which is also considered to be proportional tothe desired 16S nucleic acid levels. Absolute quantification of anucleic acid can be provided by sequencing spike-in (adding a 16Ssequence not in the sample at a known level, usually determined bydPCR/qPCR and then use the relative abundance after sequencing and theknown abundance level that was inputted as the anchor) as will beunderstood by a skilled person.

Absolute quantification of a nucleic acid can also be directed toquantify a fold difference between a first quantity of the targetnucleic acids and one or more second quantities of the same targetnucleic acid in a different environment (e.g. a sample) or in the sameenvironment at different times. In particular, absolute fold differencequantification can indicate a fold change in the nucleic acid abundancebetween two samples taken from a same environment at different times.When qPCR is used, absolute quantification can be performed by providinga calibration curve for a detected 16S rRNA based on a series ofpurified 16S rRNA standards of known concentrations, which is then usedto estimate the 16S rRNA concentration in the samples of interest andthen comparing the normalized numbers between samples to obtain a foldchange or fold difference between those samples. Alternatively, theabsolute fold difference quantification can be performed entirelywithout a standard curve. In such case, the qPCR reaction efficiency isassumed to be consistent with the previously characterized one (forexample, 95-99%) for a given set of reagents, primers, and the type ofsamples. Absolute fold difference between two (or among many samples) isthen calculated based exclusively on the Cq values and the assumed PCRefficiency value using the equations 3.1 and 3.2 of FIG. 2 (see Example1 as example of BC-qPCR: qPCR with barcoding primers).

The wording “relative quantification” of a nucleic acid such as 16S rRNAquantification indicates a quantity of a target nucleic acid relative toa quantity of a different nucleic acid. In particular, relativequantification can indicate a quantity of the target nucleic acidsrelative to the quantity of one or more nucleic acids (typically aplurality of nucleic acids) in a same environment (e.g. a sample).

In relative quantification of a 16S rRNA, a relative abundance a target16S rRNA is determined (e.g. within a group of 16S rRNAs), but theabsolute amount of 16S rRNA is not necessarily known. Accordingly,relative quantification” refers to measuring proportions (fractions, %)of target 16S sequences within the sample plurality of 16S rRNAsequences.

Relative abundances obtained by relative quantification can bemultiplied with a standard herein also identified as an “anchor”, toobtain absolute quantification value as will be understood by a skilledperson. Suitable anchors comprise a measure of an unchanging parameterin the target environment where the detection is made (e.g. a sample orsamples) such as the total concentration of cells, DNA, or amplicons asdetermined by flow cytometry or qPCR or dPCR.

The term “sample” as used herein indicates a limited quantity ofsomething that is indicative of a larger quantity of that something,including but not limited to fluids from a specimen such as biologicalenvironment, cultures, tissues, commercial recombinant proteins,synthetic compounds or portions thereof. In particular, biologicalsample can comprise one or more cells of any biological lineageincluding microbial and in particular prokaryotic cells, as beingrepresentative of the total population of similar cells in the sampledindividual. Exemplary biological samples comprise the following: wholevenous and arterial blood, blood plasma, blood serum, dried blood spots,cerebrospinal fluid, lumbar punctures, nasal secretions, sinus washings,tears, corneal scrapings, saliva, sputum or expectorate, bronchoscopysecretions, transtracheal aspirate, endotracheal aspirations,bronchoalveolar lavage, vomit, endoscopic biopsies, colonoscopicbiopsies, bile, vaginal fluids and secretions, endometrial fluids andsecretions, urethral fluids and secretions, mucosal secretions, synovialfluid, ascitic fluid, peritoneal washes, tympanic membrane aspirate,urine, clean-catch midstream urine, catheterized urine, suprapubicaspirate, kidney stones, prostatic secretions, feces, mucus, pus, wounddraining, skin scrapings, skin snips and skin biopsies, hair, nailclippings, cheek tissue, bone marrow biopsy, solid organ biopsies,surgical specimens, solid organ tissue, cadavers, or tumor cells, amongothers identifiable by a skilled person. Biological samples can beobtained using sterile techniques or non-sterile techniques, asappropriate for the sample type, as identifiable by persons skilled inthe art. Some biological samples can be obtained by contacting a swabwith a surface on a human body and removing some material from saidsurface, examples include throat swab, nasal swab, nasopharyngeal swab,oropharyngeal swab, cheek or buccal swab, urethral swab, vaginal swab,cervical swab, genital swab, anal swab, rectal swab, conjunctival swab,skin swab, and any wound swab. Depending on the type of biologicalsample and the intended analysis, biological samples can be used freshlyfor sample preparation and analysis, or can be fixed using fixative.

Exemplary samples according to the instant disclosure samples comprisetear fluid, saliva, nasal, oral, tonsillar, and pharyngeal swabs,sputum, bronchoalveolar lavage (BAL), gastric, small-intestine, andlarge-intestine contents and aspirates, feces, bile, pancreatic juice,urine, vaginal samples, semen, skin swabs, tissue and tumor biopsy,blood, lymph, cerebrospinal fluid, amniotic fluid, mammary glandsecretions/breast milk. Examples of environmental and industrialsamples: soil and other media for (agricultural) plant growth, water,sediment, oil well samples, bioreactors (e.g., complex/mixedprobiotics). Samples can also include clean room swabs, hospitalsurfaces, and mucosal brush biopsies.

In methods and systems herein described, absolute quantification of atarget 16S rRNA within a sample further comprising prokaryotes andrelated 16S rRNA (herein also sample 16S rRNA) is performed throughdetection of a 16S rRNA recognition segment.

In methods and systems of the present disclosure a 16S rRNA recognitionsegment can be a 16S rRNA polyribonucleotide or 16S rRNA DNA as will beunderstood by a skilled person. Selection between a 16S rRNApolyribonucleotide or 16S rRNA DNA can be performed based on theexperimental design and features of the sample, 16S rRNA segment andrelated amplifying and sequencing. For example since 16S rRNA is100-10,000 times more abundant per cell than 16S rRNA gene, 16S rRNA canbe preferred in samples with very low microbial loads. Additionally,since abundance of 16S rRNA polyribonucleotide per cell varies with thegrowth state a 16S rRNA polyribonucleotide recognition segment can beused to obtain as an indication of the growth state of a taxon. A higherratio of 16S rRNA polyribonucleotide to 16S rRNA gene can indicate ahigher growth rate for that taxon. Furthermore, in general 16S rRNApolyribonucleotide is much less stable than 16S rRNA gene and thus canbe used as a live/dead marker for a taxon. A low or zero level of 16SrRNA polyribonucleotide in the presence of 16S rRNA gene can indicatethat the taxon was not alive in the sample. Additional features,reaction constraints in connection with quantification of target 16SrRNA can be identified by a skilled person upon reading disclosure.

The term “target” as used herein indicates a reference item (such as anucleic acid and/or a prokaryote) that is aim of a method, step orreaction herein described.

In methods and systems herein described a target 16S rRNA comprises a16S rRNA recognition segment in which a 16S rRNA variable regionspecific for the target 16S rRNA is flanked by 16S rRNA conservedregions specific for a plurality of sample 16S rRNA, the plurality ofsample 16S rRNAs comprising the target 16S rRNA.

In embodiments herein described a “16S rRNA recognition segment”indicates a region of 16S rRNA comprising a variable region flanked bytarget conserved regions each independently having 8 to 50 bp in variousconfigurations as will be understood by a skilled person upon reading ofthe present disclosure.

The term “flank” and “flanking” as used herein with respect to regionsof RNA and/or DNA indicates a polynucleotide configuration where“flanking” segment/sequences are located at both sides of a “flanked’reference segment/sequence. The “flanking” segment/sequences cancomprise a same or different sequence, be adjacent to the secondreference sequence and/or separated by an intermediate sequence as willbe understood by a skilled person. In some embodiments, the two flankingregions are no more than 500 bp apart.

In some embodiments, the 16S rRNA recognition segment can compriseadditional conserved regions between the two target conserved regions.In some embodiments, the 16S rRNA recognition segment can comprise aplurality of variable regions interspaced by conserved regions in aconfiguration wherein conserved regions are located in between one ormore variable regions as will be understood by a skilled person.

In particular in a 16S rRNA recognition segment according to thedisclosure, a polynucleotide typically up to 1,500 bp comprises two ormore conserved regions of the 16S rRNA gene sequences flanking one ormore variable regions. For example the one or more variable regions cancomprise one or more of nine hypervariable regions V1-V9 ranging fromabout 30 to 100 base pairs long in various configurations, in whichconserved regions can flank and possibly be interspersed betweenvariable V1-V9 regions as will be understood by a skilled person. Thenumber of variable and conserved regions and related configuration isdetermined by the 16S rRNA to be quantified by methods and systems ofthe present disclosure.

In particular, in the 16S rRNA recognition segment, variable 16S rRNAregions can be selected to provide signature sequences unique for atarget taxon and useful for identification of the target taxon and/orcorresponding 16S rRNA.

In the 16S rRNA recognition segment, the 8 to 50 bp 16S rRNA conservedregions can be selected to provide sequences conserved in the majorityof the prokaryotes within a taxa of a taxonomic rank higher than thetarget taxon (e.g. a sample prokaryotic taxa known or expected topossibly be comprised in a microbial community of target environment)Different degrees of conservation in the conserved sequence allowgrouping of different prokaryotes. The degree of conservation alsovaries widely between hypervariable regions, with more conserved regionscorrelating to higher-level taxonomy and less conserved regions to lowerlevels, such as genus and species. In some embodiments, the variableregion ideally has a unique sequence between the two conserved regionsfor each species of interest.

In embodiments described, the 16S rRNA segment can comprise a pluralityof 16S rRNA segment of a same length but typically different lengths,each having the same 8 to 50 bp conserved regions and a same ordifferent type and number of variable regions.

In some embodiments, in a 16S r RNA recognition segment two conservedregions are <500 nt apart from each other and therefore a plurality of16S r RNA recognition segment can independently have a length <500 nt,higher than <1,000 nt. In some embodiments, the 16S rRNA recognitionsegment can comprise the entire 16S rRNA sequence. The maximum length ofa 16S rRNA recognition is limited by the sequencing technology as willbe understood by a skilled person. For example nanopore sequencingperforms “long read” sequencing which allows sequencing the whole˜1500-1600 nts of the 16S rRNA. Other sequencing methods can limit thelength of the 16S rRNA to approximately up to 600 nts and preferably upto 500 nts, of variable regions between the flanking target conservedregions, in a 16S rRNA recognition segment of up to 650 nts or 550 nts.

In embodiments, herein described absolute quantification of a target 16SrRNA within a sample is performed through detection of a 16S rRNArecognition segment using primers specific for conserved regions of the16S rRNA recognition segment which are conserved within plurality ofsample 16S rRNAs and related taxa, if any can be associated to theselected plurality of 16S rRNAs.

The wording “primer” as used herein indicates a short, single-strandedpolynucleotide configured to complementary and capable of complementarybinding a target polynucleotide region. Primers in the sense of thedisclosure can be used to define the region of the DNA that will beamplified in PCR reactions and/or sequencing reactions. Primers are alsoreferred to as oligonucleotides. Typically, a primer can range in lengthfrom 8-50 nucleotides, most preferred between 15-25 nucleotides (e.g. 20nts) as will be understood by a skilled person.

The term “polynucleotide” as used herein indicates an organic polymercomposed of two or more monomers including nucleotides, nucleosides oranalogs thereof. The term “nucleotide” refers to any of severalcompounds that consist of a ribose or deoxyribose sugar joined to apurine or pyrimidine base and to a phosphate group and that is the basicstructural unit of nucleic acids. The term “nucleoside” refers to acompound (such as guanosine or adenosine) that consists of a purine orpyrimidine base combined with deoxyribose or ribose and is foundespecially in nucleic acids. The term “nucleotide analog” or “nucleosideanalog” refers respectively to a nucleotide or nucleoside in which oneor more individual atoms have been replaced with a different atom or awith a different functional group. Exemplary functional groups that canbe comprised in an analog include methyl groups and hydroxyl groups andadditional groups identifiable by a skilled person. Exemplary monomersof a polynucleotide comprise deoxyribonucleotide, ribonucleotides, LNAnucleotides and PNA nucleotides as will be understood by a skilledperson.

Accordingly, the term “polynucleotide” includes nucleic acids of anylength, and in particular DNA, RNA, analogs thereof, such as LNA andPNA, and fragments thereof, each of which can be isolated from naturalsources, recombinantly produced, or artificially synthesized.Polynucleotides can typically be provided in single-stranded form ordouble-stranded form (herein also duplex form, or duplex). A“single-stranded polynucleotide” refers to an individual string ofmonomers linked together through an alternating sugar phosphatebackbone. The 5′-end of a single strand polynucleotide designates theterminal residue of the single strand polynucleotide that has the fifthcarbon in the sugar-ring of the deoxyribose or ribose at its terminus(5′ terminus). The 3′-end of a single strand polynucleotide designatesthe residue terminating at the hydroxyl group of the third carbon in thesugar-ring of the nucleotide or nucleoside at its terminus (3′terminus). A “double-stranded polynucleotide” or “duplex polynucleotide”refers to two single-stranded polynucleotides bound to each otherthrough complementarily binding. The duplex typically has a helicalstructure, such as a double-stranded DNA (dsDNA) molecule or a doublestranded RNA, which is maintained largely by non-covalent bonding ofbase pairs between the strands and by base stacking interactions. Theterm “5′-3′ terminal base pair” with reference to a duplexpolynucleotide refers to the base pair positioned at an end of theduplex polynucleotide that is formed by the ′5 end of one single strandof the two single strand forming the duplex polynucleotide base-pairedwith the 3′ end of the single strand forming the duplex polynucleotidecomplementary to the one single strand.

The term “complementary” as used herein indicates a property of singlestranded polynucleotides in which the sequence of the constituentmonomers on one strand chemically matches the sequence on another strandto form a double stranded polynucleotide. Chemical matching indicatesthat the base pairs between the monomers of the single strand can benon-covalently connected via two or three hydrogen bonds withcorresponding monomers in the another strand. In particular, in thisdisclosure, when two polynucleotide strands, sequences or segments arenoted to be complementary, this indicates that they have a sufficientnumber of complementary bases to form a thermodynamically stabledouble-stranded duplex. Double stranded of complementary single strandedpolynucleotides include dsDNA, dsRNA, DNA: RNA duplexes as well asintramolecular base paring duplexes formed by complementary sequences ofa single polynucleotide strand (e.g., hairpin loop).

The terms “complementary bind”, “base pair”, and “complementary basepair” as used herein with respect to nucleic acids indicates the twonucleotides on opposite polynucleotide strands or sequences that areconnected via hydrogen bonds. For example, in the canonical Watson-CrickDNA base pairing, adenine (A) forms a base pair with thymine (T) andguanine (G) forms a base pair with cytosine (C). In RNA base paring,adenine (A) forms a base pair with uracil (U) and guanine (G) forms abase pair with cytosine (C). Accordingly, the term “base pairing” asused herein indicates formation of hydrogen bonds between base pairs onopposite complementary polynucleotide strands or sequences following theWatson-Crick base pairing rule as will be applied by a skilled person toprovide duplex polynucleotides. Accordingly, when two polynucleotidestrands, sequences or segments are noted to be binding to each otherthrough complementarily binding or complementarily bind to each other,this indicate that a sufficient number of bases pairs forms between thetwo strands, sequences or segments to form a thermodynamically stabledouble-stranded duplex, although the duplex can contain mismatches,bulges and/or wobble base pairs as will be understood by a skilledperson.

The wording “specific” “specifically” or “specificity” as used hereinwith reference to the binding of a first molecule to second moleculerefers to the recognition, contact and formation of a stable complexbetween the first molecule and the second molecule, together withsubstantially less to no recognition, contact and formation of a stablecomplex between each of the first molecule and the second molecule withother molecules that may be present. Exemplary specific bindings areantibody-antigen interaction, cellular receptor-ligand interactions,polynucleotide hybridization, enzyme substrate interactions andadditional interactions identifiable by a skilled person. The wording“specific” “specifically” or “specificity” as used herein with referenceto a computer supported tool, such as a software indicates a toolcapable of identifying a target sequence (such as the nucleic acids ofthe target organism herein described) among a group of sequences e.g.within a database following alignment of the target sequence with thesequences of the database. Exemplary software configured to specificallydetect target sequences comprise Primer-3 [50-52], PerlPrimer [53] andPrimer-BLAST [54].

In embodiments of method and systems herein described, the wording“specific” when used in connection with a primer and a target sequenceindicates a primer capable of complementary bind the target sequenceforming a duplex polynucleotide more thermodynamically stable under areaction condition than other duplex polynucleotides resulting fromcomplementary binding of the primers with additional polynucleotidespossibly present.

The term “thermodynamic stability” as used herein indicates a lowestenergy state of a chemical system. Thermodynamic stability can be usedin connection with description of two chemical entities (e.g., twomolecules or portions thereof) to compare the relative energies of thechemical entities. For example, when a chemical entity is apolynucleotide, thermodynamic stability can be used in absolute terms toindicate a conformation that is at a lowest energy state, or in relativeterms to describe conformations of the polynucleotide or portionsthereof to identify the prevailing conformation as a result of theprevailing conformation being in a lower energy state. Thermodynamicstability can be detected using methods and techniques identifiable by askilled person. For example, for polynucleotides thermodynamic stabilitycan be determined based on measurement of melting temperature T_(m),among other methods, wherein a higher T_(m) can be associated with amore thermodynamically stable chemical entity as will be understood by askilled person. Contributors to thermodynamic stability can include, butare not limited to, chemical compositions, base compositions,neighboring chemical compositions, and geometry of the chemical entity.

The strand melting temperature (Tm) of the double-stranded duplex formedby the primer and a target polynucleotide region can be experimentallytested or measured.

In methods and systems herein described, primers used to performabsolute quantification of a target 16S rRNA, are engineered to comprisea target primer sequence specific for a target conserved regions of a16S rRNA recognition segment which are conserved in a plurality ofsample 16S rRNA and flank variable regions which re conserved in thetarget 16S rRNA.

In methods and systems herein described, selection of specific primersto quantify a target 16S rRNA with the method of the disclosure cancomprise for example selecting candidate primers in silico beforeexperimentally testing specificity of candidate primers selected insilico. For example, the performance of a primer can be tested in silicoby running in silico PCR on the SILVA database using the TestPrimefunction [9, 55, 56]. From the results of the PCR, the program computescoverages for each taxonomic group in the taxonomies offered by SILVA.These coverages can then be inspected so that one can identify strengthsand weaknesses of a particular pair of primers. In addition or in thealternative, in silico testing can be performed by BLAST or Primer BLASTagainst any expected off-target DNA potentially present in the samplesuch as human genomic or mitochondrial DNA to guide the optimizationagainst the off-target amplification.

In methods and systems herein described, selection of specific primersto quantify a target 16S rRNA with the method of the disclosure cancomprise modifying the target primer sequence of a base primer to modifythe related specificity for a target conserved region in the 16S rRNArecognition segment. The base primer can be known primer or a candidateprimer selected in silico and/or experimentally.

In methods and systems herein described, modification of a base primerto quantify a target 16S rRNA with the method of the disclosure themodification can include modification of the length with an increasedlength providing higher specificity for the template and a shorterlength providing lower specificity as will be understood by a skilledperson. Typically the length of the primer is selected to maintainspecificity of the sample 16S rRNA taking account potential mismatcheswith the -target regions of the 16S rRNA potentially present in thesample due to the diversity of 16S rRNA within the sample 16S rRNAs.Accordingly primers of the disclosure can comprise a primer targetsequence with a degeneracy depending on the taxonomic coverage desired.In embodiments herein described, the length of the targeting sequencecomplimentary to the target sequences of the target 16S rRNA recognitionsegment is typically 15-25 nucleotides.

In methods and systems herein described, modification of a base primerto quantify a target 16S rRNA with the method of the disclosure, themodification can include modification of the sequences by introducingmismatches or reducing degeneracy to narrow the taxonomic coverage ofthe target conserved regions or to improve primer specificity againstoff-target amplification (e.g., human or animal genomic or mitochondrialDNA). The modification can include modification of the sequences byintroducing degenerate bases and/or universal bases to broaden thetaxonomic coverage of the target conserved regions. Exemplary“universal” bases comprise as 2′-deoxylnosine, 2′-deoxynebularine,3-nitropyrrole 2′-deoxynucleoside, 5-nitroindole 2′-deoxynucleoside.Primer mismatches with the known off-target DNA (e.g., human or animalgenomic or mitochondrial DNA) can be reinforced by using primerscomprising modified nucleotides (e.g. LNA) which would increase theprimer specificity against off-target amplification.

In particular in embodiments herein described, the target sequence ofthe primer should be designed target amplification to preferably have nomore than 3 mismatches with the target 16S rRNA sequence and nomismatches at the 3′ end of the sequence. In most preferred embodiments,the primers have no mismatches with the target conserved sequence of the16S rRNA recognition segment, throughout the entire length of the targetsequence of the primer.

In methods and systems herein described, modification of a base primerto quantify a target 16S rRNA with the method of the disclosuremismatches can be introduced in base primers to increase specificity ofthe primers for the target 16S rRNA with respect to known off-targetsequences of contaminant polynucleotides potentially present in thesample (e.g., host genomic or mitochondrial DNA). Accordingly, primerscan be designed such that those mismatches between the primer and theoff-target template. Preferably mismatched are located closer to the 3′end of the primer target sequence to provide stronger specificity forthe target template during amplification.

In methods and systems herein described, modification of a base primerto quantify a target 16S rRNA with the method of the disclosure, themodification can include modification of the sequences and/or length ofthe primers in view of a target annealing temperature and/or otherconditions. Preferably forward and reverse primers used herein aredesigned to have annealing temperature as close to each other aspossible. Exemplary annealing temperatures range from 45° C. to 75° C.,preferably 60° C.

In methods and systems herein described, modification of a base primerto quantify a target 16S rRNA with the method of the disclosure,selection of the primers can be performed to optimize use of thespecific primers in combination with additional primers that can be usedin the amplifying and/or sequencing such as a TagMan probe that targetsa conserved region between the two conserved regions targeted by theforward and reverse primers in 16S rRNA. In general, TaqMan probes aredesigned to have a higher annealing temperature (˜5° C.) compared withprimers that can be used in combination with the TaqMan probes as willbe understood by a skilled person.

In methods and systems herein described, modification of a base primerto quantify a target 16S rRNA with the method of the disclosure,selection of the primers can be performed to optimize the sequencingstep of the method. For example, in some embodiments, primers flank anamplicon region of a length that configured to perform for a single- orpaired-end amplicon sequencing using a desired next generationsequencing technique. In some cases, the amplicon region is within 500bp, while in other cases longer regions can also be amplified andsequenced by Nanopore sequencing [57-59].

In methods and systems herein described, modification of a base primerto quantify a target 16S rRNA with the method of the disclosure,selection of the primers can be performed to optimize specificity of theprimers to exclude polynucleotide possibly present in the sample otherthan 16S rRNA (herein also contaminant polynucleotides) such aspolynucleotide of host cells when the target environment hosting amicrobial community is an individual, or polynucleotide present in atarget environment outside the individual.

Computational methods can be used to perform in silico testing and/ormodification of candidate primers targeting the conserved regions of 16SrRNA gene DNA herein described. Exemplary computational methods includemopo16S [60]. For example, modifying a base primer such as the EMP(Earth Microbiome Project) forward primer by shortening it resulted inincreasing its microbial coverage.

In some embodiments, the target primer sequence of the 16S rRNA primersused in method and systems of the disclosure is a modified version of abase primer sequence, such as EMP primer and/or a TaqMan probe.

In particular, in an exemplary embodiments primers specific for a target16S rRNA can be engineered from a EMP primer set in which the EMPforward primer at its 5′ end is redesigned to start at the position 519of the V4 region of microbial 16S rRNA gene sequence.

For example in an exemplary embodiment the 16S rRNA primers can be madespecific for the target 16S rRNA by redesigning the EMP forward primerso that the nonspecific annealing to the host rRNA such as the mouse andhuman mitochondrial 12S rRNA gene DNA will be reduced or eliminated,which is the main competing template of mammalian origin identified byamplicon sequencing of PCR products obtained with mouse germ-free tissueDNA. Such change increases the primer's specificity for low copy numbermicrobial templates in samples with high content of mouse or human hostDNA background (see Examples 2 and 3).

In some of those embodiments, the modification of the EMP primer setbroadened its taxonomical coverage of the microbial diversity (86.0%Archaea, 87.0% Bacteria) compared with the original EMP primer set(52.0% Archaea, 87.0% Bacteria) based on the SILVA (version 132) 16SrRNA gene sequence reference database [9, 55, 56] (Example 2 and Example3). The broader coverage of microbial diversity maximizes thecompleteness of microbial detection and quantification and richness ofdiversity profiling.

In some embodiments, a primer set has been obtained specific for allprokaryotes comprises a forward primer having a sequence of5′-CAGCMGCCGCGGTAA-3′) (SEQ ID NO: 26) and a reverse primer having asequence of 5′-GGACTACHVGGGTWTCTAAT-3′ (SEQ ID NO: 27).

Broad-coverage universal 16S primers can be optimized for higherspecificity for a taxon of choice. In particular, one introducesnucleotide substitutions or eliminates degeneracy according to theconsensus sequence of the conserved priming sites of the taxon ofinterest. The length of the primers can be adjusted for them to extendinto less overall conserved regions but conserved for the taxon ofinterest. The position of the primers can be adjusted to extend andcover the less overall conserved regions but conserved for the taxon ofinterest. The above approaches can be combined with the optimizationagainst the known potential off-target templates likely present in thesample (e.g. human genomic or mitochondrial DNA). Alternatively, ataxon-specific TaqMan probe can be introduced which can be used incombination with the same forward and reverse primers with broadcoverage. Absolute quantification (dPCR or qPCR) will be based on theTaqMan probe signal.

Table 1 includes an exemplary list from [61] of universal and specificprimers for 16S rRNA gene in some microbial groups.

TABLE 1 SEQ ID Primer Sequence (5'-3') Target Group Reference NO 8FAGAGTTTGATCCTGGCTCAG Universal [62] 28 27F AGAGTTTGATCMTGGCTCAGUniversal [63] 29 CYA106F CGGACGGGTGAGTAACGCGTG Cyanobacteria [64] 30 ACC[F] CCAGACTCCTACGGGAGGCAG Universal [65] 31 C 357F CTCCTACGGGAGGCAGCAG[62] 32 CYA359F GGGGAATYTTCCGCAATGGG Cyanobacteria [64] 33 515FGTGCCAGCMGCCGCGGTAA Universal [62] 34 533F GTGCCAGCAGCCGCGGTAA Universal[66] 35 895F CRCCTGGGGAGTRCRG Bacteria exc. [67] 36 plastids &Cyanobacteria 16S.1100. CAACGAGCGCAACCCT Universal [62] 37 F16 1237FGGGCTACACACGYGCWAC Universal [62] 38 519R GWATTACCGCGGCKGCTG Universal[62] 39 CYA781R GACTACWGGGGTATCTAATCC Cyanobacteria [64] 40 CWTT CD[R]CTTGTGCGGGCCCCCGTCAATT Universal [65] 41 C 902R GTCAATTCITTTGAGTTTYARYBacteria exc. [67] 42 C plastids & Cyanobacteria 904RCCCCGTCAATTCITTTGAGTTTY Bacteria exc. [67] 43 AR plastids &Cyanobacteria 907R CCGTCAATTCMTTTRAGTTT Universal [63] 44 1100RAGGGTTGCGCTCGTTG Bacteria [62] 45 1185mR GAYTTGACGTCATCCM Bacteria exc.[67] 46 plastids & Cyanobacteria 1185aR GAYTTGACGTCATCCA Lichen- [67] 47associated Rhizobiales 1381R CGGTGTGTACAAGRCCYGRGA Bacteria exc. [67] 48Asterochloris sp. plastids 1381bR CGGGCGGTGTGTACAAGRCCY Bacteria exc.[67] 49 GRGA Asterochloris sp. plastids 1391R GACGGGCGGTGTGTRCAUniversal [62] 50 GGTTACCTTGTTACGACTT Universal [62] 51 1492R(s)ACCTTGTTACGACTT Universal [63] 52

Additional primers that can be used as base primers to select specificprimers suitable in methods and systems of the disclosure areidentifiable by a skilled person upon reading of the present disclosurein view of specific target 16S rRNA and sample 16S rRNA.

In some embodiments, a method of the disclosure comprises amplifying thetarget 16S rRNA recognition segment by performing polymerase chainreaction (PCR) on nucleic acids from the sample with primers specificfor the 16S rRNA conserved regions to quantitatively detect an absoluteabundance of the plurality of sample 16S rRNAs in the sample and toprovide an amplified 16S rRNA recognition segment.

In embodiments wherein the 16S rRNA is a polyribonucleotide, specificprimers are used for generation of cDNA from an RNA template via reversetranscription. In particular, in those embodiments a primer can be usedto reverse transcription which is a reverse primer used in amplifyingstep according to the instant disclosure as will be understood by askilled person.

The term “amplify” or “amplification” as used herein indicates a usuallymassive replication of a polynucleotide in particular of a gene or DNAsequence. Accordingly, amplifying indicated in connection with areference polynucleotide indicates the replication of the referencedpolynucleotide to provide a greater number of the referencedpolynucleotide and increase representation of the referencepolynucleotide in a target environment. Amplification can be conductedthrough methods such as: Polymerase Chain Reaction, ligase chainreaction, transcription-mediated amplification, methods and additionalmethods identifiable by a skilled person. Copies of a particular nucleicacid sequence generated in vitro in an amplification reaction are calledamplicons or amplification products.

In embodiments of the disclosure amplification is performed byPolymerase Chain Reaction (PCR) on nucleic acids extracted from thesample.

The term “polymerase chain reaction” as used herein indicates a reactionamplifying copies a polynucleotide in a series of cycles of temperaturechanges. In particular, in various PCR methods repeated cycles ofheating and cooling exposes reactants of temperature-dependent reactionswhich result in amplification of the polynucleotide. PCR can amplifypolynucleotides of up to 40 kilo base pairs (kbp) and typicallyamplifies between 0.1 and 10 kbp in length, as will be understood by askilled person.

In all PCR methods the amplification is performed by using primers and apolymerase. The term “polymerase” as used herein indicates an enzymecapable of synthesizes long chains of polymers or nucleic acids,replicating a target polynucleotide or template strand usingbase-pairing interactions. Exemplary polymerase comprises heat stableDNA polymerase such as Taq polymerase or high fidelity polymerases suchas Pfu polymerase. Commercial modification of these base polymerases andtheir associated master mixes work well (e.g., Bio-Rad SsoFast EvaGreenSupermix (Bio-Rad Laboratories, Hercules, Calif.), 5PRIME HotMaster TaqDNA Polymerase and 5PRIME HotMasterMix (Quantabio, Beverly, Mass.), KAPAHiFi polymerase (KAPA Biosystems, Woburn, Mass.), JumpStart Taq DNAPolymerase (Sigma-Aldrich, St. Louis, Mo.).

Accordingly, in a PCR reaction the primers determine the region oftarget polynucleotide that will be copied or amplified. In particular,in a PCR a forward primer contains a nucleotides complementary andcapable of complementary binding a region of the target polynucleotideupstream of the sequence to be amplified, and a reverse primer containsnucleotides complementary and capable of complementary bindingnucleotides on the target polynucleotide that are downstream of thesequence to be amplified as will be. Upstream refers to a 5′ location tothe sequence to be amplified relative to the coding strand anddownstream refers to a 3′ location to the sequence to be amplifiedrelative to the coding strand as will also understood by a skilledperson.

In embodiments of the present disclosure polymerase chain reaction (PCR)is performed with primers specific for the 16S rRNA conserved regions toquantitatively detect an absolute abundance of the plurality of sample16S rRNAs in the sample and to provide an amplified 16S rRNA recognitionsegment. For example, various combinations of the forward and reverseprimers from Table 1 can be used to amplify various regions of 16S rRNAgene sequences. Using various combinations of such primers will resultin variable taxonomic coverage of the microbial diversity and variabletaxonomic resolution as will be understood by a person skilled in theart.

In some embodiments, primers specific for a target sequence of the 16SrRNA to be used in methods and systems of the disclosure, can furthercomprises in addition to the target primer sequence described above, abarcode and/or an adapter sequence.

The term “barcode” or “barcoding” when used as a verb with reference toa reaction, indicates a reaction performed to covalently attach abarcode in the sense of the disclosure to the reference item, in aconfiguration allowing detection of the barcode. Accordingly, barcodingin the sense of the disclosure refers to coupling a unique set of tagsor identifiers in order to mark molecules for downstream detection andidentification. In particular, in embodiments herein described barcodingin particular refer to a coupling reaction of molecules within a samesample in case multiple samples are provided for analysis in order tolabel these molecules for downstream detection and identification. Insome embodiments, suitable tags or identifiers for barcoding can beoligonucleotide label. As used herein, “unique” means different from anyother. Exemplary reactions that can be used to barcode a molecule in thesense of the disclosure comprise ligation binding of antibody covalentlyattaching an oligonucleotide, addition of DNA by transposase andadditional reactions identifiable by a skilled person.

In some embodiments, a barcode can be obtained by sequential directcovalent linkage of a tag with another tag until formation of a barcodecomprising a series of two or more tags directly attached one to anotherthrough covalent linkage.

In embodiments herein described, the primer construct can also containan adapter sequence, and in particular an adapter compatible to anext-generation sequencing platform.

An “adapter or a linker is a short, chemically synthesized,single-stranded or double-stranded oligonucleotide that can be ligatedto the ends of other DNA or RNA molecules. An adapter can be designed tocomprise overhangs specific to the complementary sequence of the targetmolecule of interest. The overhang can be used for subsequent processingof the nucleic acid and/or protein complex for tagging, ligation,elongation, and additional downstream analysis as will be understood bya skilled person. The overhang sequence can be at least 1 bp in length.The adapter sequence can be located at one or both ends of other DNA orRNA molecules. In some embodiments, the barcode is ligated onto nucleicacids with a DNA or RNA ligase via an adapter as will be understood by aperson skilled in the art. Overhangs can be generated by restrictiondigestion as will be understood by a skilled person.

In some embodiments, a primer can comprise a specific adapter sequenceligated to the 5′ end of the target specific sequence portion of theprimer. This adapter (also referred to as a sequencing adapter) is ashort oligonucleotide of known sequence that can provide a priming sitefor both amplification and sequencing of the target nucleic acid. Assuch, adapters allow binding of a fragment to a flow cell for nextgeneration sequencing.

Any adapter sequence required by a sequencing platform of a choice canbe included in a primer used herein. In some embodiments of the method,the adapter sequence is an Illumina P5 adapter, P7 adapter, P1 adapter,A adapter, or Ion Xpress™ barcode adapter.

In some embodiments, a primer set used herein can further comprise alinker and/or pad sequence suitable for next-generation sequencing aswill be understood by a skilled person. The primer pad sequence is usedto extend the region over which the sequencing primer anneals andincreases the T_(m) of the sequencing primer to fit that of thesequencing platform such as Illumina platform as will be understood by askilled person.

In particular, in some embodiments, a primer can be engineered forBarcoding qPCR (BC-qPCR) a single-step amplicon barcoding-quantificationin which a qPCR reaction performed with primers that include barcodeand/or adapter sequences. Additionally, BC-qPCR can be run with asingle-step (one set of primers carrying barcodes and adapters) [16, 18]and two(multi)-step (two sets of primers: 1st set carrying commonadapters, 2nd set carrying barcodes and sequencing adapters) [68-70].

In embodiments of the methods and systems herein described, a primer setspecific for the 16S rRNA conserved regions of the 16S rRNA recognitionsegment, and preferably comprising a barcode and/or an adaptor, is usedto perform the amplifying the 16S rRNA recognition segment by performingpolymerase chain reaction to obtain a total number of the sample 16SrRNA.

In some embodiments herein described, the PCR reaction is set up with 16S rRNA gene primer containing the target primer sequence specific forthe conserved regions in the 16S rRNA recognition segments together withbarcodes, adapters, linker, pad, and/or frameshifting sequencesconfigured for next-generation sequencing (see e.g. Example 1 and “B” inFIG. 1).

The PCR reaction mix further comprises conventional commercial reagentsfor 16S rRNA gene amplicon library preparation as will be understood bya person skilled in the art. Reactions can be run in replicates toimprove the real-time PCR quantification precision and resolution andamplicon barcoding uniformity [18].

In some embodiments, the parameters used in the PCR and/or barcoding-PCRprocedures herein described are optimized to minimize primer dimerformation and host DNA amplification while reducing amplification biasesand ensuring uniform amplification of diverse 16S rRNA gene sequencesfrom complex microbiomes.

In some embodiments, the amplification PCR reaction is conducted at thehighest possible annealing temperature to minimize the primer dimerformation and non-specific host mitochondrial DNA amplification both ofwhich would be competing with specific prokaryote 16S rRNA gene DNAtemplate for reaction resources (see Example 3). In some embodiments,the temperature of 40-80° C. is selected as optimal for the PCRreaction, preferably between 50-70° C., and more preferably about 60° C.In some of those embodiments, even in the presence of high host DNAbackground, the reaction efficiency is ˜95.0% and the assay is able toresolve 1.25 to 1.67-fold differences in total 16S rRNA gene copy loadsamong samples within the range of ˜10^(4.83)-10^(10.95) copies/mL (seeExample 3).

99 In methods and systems herein described the amount of amplifiedproduct which is determined by the available substrates in the reaction,which become limiting as the reaction progresses will provide absoluteabundance of the plurality of sample 16S rRNAs in the sample and anamplified 16S rRNA recognition segment (16S rRNA amplicon) as will beunderstood by a skilled person.

In embodiments wherein the primer set used in the amplifying, is anadapter-ligated and/or barcoded primers, the adapter sequence and/orbarcode sequence are incorporated into the 16S rRNA amplicon along withthe target 16S rRNA primer sequence during amplification. Therefore, theresulting amplicons comprise both the 16S rRNA target sequence and thebarcode and/or adapter sequence, which are suitable for the subsequentsequencing and do not require the traditional library preparationprotocol.

In some embodiments wherein the primer set used in the amplifying,comprises a barcode, the presence of the barcode also permits thedifferentiation of sequences from multiple sample sources, the amplified16S rRNA derived from a single sample further comprise an identicalbarcode sequence that indicates the source from which the amplicon isgenerated, the barcode sequence for each sample being different from thebarcode sequences from all other samples. As such, in those embodiments,the use of barcode sequences permits multiple samples to be pooled persequencing run and the sample source subsequently ascertained based onthe barcode sequence. In some embodiments, the 16S rRNA amplificationand barcoding is performed simultaneously in one set up to generate abarcoded amplicon library that can be used in sequencing directed todetect a relative abundance of the target 16S rRNA with respect to theplurality of sample 16S rRNAs in the sample according to the methods andsystems of the present disclosure (see Example 1).

In embodiments wherein the primer set used in the amplifying, comprisesan adapter, amplicons corresponding to specific regions of 16S rRNA areamplified using primers that contain an oligonucleotide sequencingadapter to produce adapter tagged amplicons to be used in sequencingdirected to detect a relative abundance of the target 16S rRNA withrespect to the plurality of sample 16S rRNAs in the sample according tothe methods and systems herein described.

In embodiments, wherein the primer set used in the amplifying does notcontain an a barcode and/or adapter sequences, the amplicons producedcan be ligated to an oligonucleotide sequencing adapter on one or bothends of the amplicons as will be understood by a person skilled in theart to allow sequencing directed to detect a relative abundance of thetarget 16S rRNA with respect to the plurality of sample 16S rRNAs in thesample according to the methods and systems herein described [71, 72].

In embodiments, wherein the primer set used in the amplifying does notcontain a barcode and/or adapter sequences, a two-step recognitionsegment amplification and barcoding can be performed consisting of twoconsecutive PCR steps as previously described [69, 73, 74]. The firstPCR step uses a pair of primers that have two parts: sequences targetingthe 16S rRNA and adapter overhangs. The second PCR step uses a pair ofprimers that target the adapter sequences added to the 16S amplicons inthe first step and also carry barcodes and flow-cell adapters on theirends [45] as will be understood by a skilled person.

In embodiments wherein the 16S rRNA is a 16S rRNA polyribonucleotide,RNA templates can first be reverse transcribed into cDNA beforefollowing the same amplification steps described for DNA. The reversetranscription step generally consists of a reverse transcriptase enzyme,associate buffers, dNTPs, and an RNase inhibitor.

In embodiments wherein the 16S rRNA is a 16S rRNA polyribonucleotide,exemplary reverse transcriptase enzymes consist of the base and modifiedenzymes of the following forms: Bst, M-MLV, AMV, and HIV-1.Additionally, thermostable reverse transcriptases can be used (eg.,RapiDxFire (Lucigen), RocketScript (Bioneer)). Associated buffers aregenerally provided by the manufacturer but can also be homemade mixturesof salts. dNTP mixtures contain the fours nucleotides (dATP, dCTP, dGTP,and dTTP) at equimolar concentration. RNase inhibitors consist of smallmolecules or enzymes that inhibit the activity of RNAse enzymes (e.g.,RNase A, RNase B, RNase C).

In embodiments wherein the 16S rRNA is a 16S rRNA polyribonucleotide,the reverse transcription thermocycling step can include a lowtemperature (20-50 C) primer annealing step followed by the mandatorycDNA synthesis step. cDNA synthesis is run at a temperature rangespecific to each reverse transcriptase enzyme. In general cDNA synthesisoccurs between 40 C and 80 C with thermostable enzymes preferring 60-80C and non-thermostable enzymes preferring 40-55 C. Most preferably 70 Cand 55 C respectively. The cDNA synthesis step is generally run for5-120 mins, more preferably, 10-60 mins. A heat denaturation step of theRT enzyme can be included as well which occurs between 70-95 C,preferably 80 C.

In embodiments wherein the 16S rRNA is a 16S rRNA polyribonucleotide,the primer concentration in the reverse transcription step can rangefrom 100 nM-25 uM, more preferably 500 nm-1 uM.

In some embodiments wherein the 16S rRNA is a 16S rRNApolyribonucleotide, a reaction clean-up step can be performed followingcDNA synthesis before the DNA amplification, barcoding, andquantification steps. The clean-up generally consists of either a silicacolumn based or magnetic bead based clean-up similar to those describedin DNA extraction.

In some embodiments of the methods and systems herein described,amplifying the 16S rRNA recognition segment can be performed withtouchdown PCR a PCR method that can be used for increased specificityand sensitivity in PCR amplification as will be understood by a personskilled in the art [75].

In some embodiments of the methods and systems herein described,amplifying the 16S rRNA recognition segment can be performed with qPCR.The term “qPCR”,“quantitative polymerase chain reaction”, “real-timepolymerase chain reaction” or “real-time PCR’ as used, herein indicatedindicates a polymerase chain reaction performed to monitor amplificationof a target polynucleotide during the PCR (in real time). qPCR can beused to detect a target polynucleotide quantitatively (quantitativereal-time PCR) or semi-quantitatively (above/below a certain amount oftarget polynucleotide) (semi-quantitative real-time PCR). Typically inqPCR monitoring is performed through use of non-specific fluorescentdyes that intercalate with any double-stranded DNA or sequence-specificpolynucleotide probes consisting of oligonucleotides that are labelledwith a fluorescent reporter, which permits detection only afterhybridization of the probe with its complementary sequence.

In embodiments of the methods and systems herein described wherein theamplifying the 16S rRNA recognition segment is performed with qPCR, thecycling conditions can be optimized. Generally, the thermocyclingprogram can be set up as follows: initial denaturing at 95° C. for 30s,annealing at 54° C. for 30s, and final extension at 68° C. for 30s. Theconcentration of primers can also be changed. In general, aconcentration of primers is in a range between 100 nM and 2.5 μMdepending on the primers and detection methods, and is typically around500 nM.

In embodiments of the methods and systems herein described wherein theamplifying the 16S rRNA recognition segment is performed with rreal-time PCR, real-time fluorescence monitoring also enablesterminating the amplification of each sample upon reaching themid-exponential phase to maximize the amplicon yield and minimize theover-amplification artifacts [76].

In embodiments of the methods and systems herein described wherein theamplifying the 16S rRNA recognition segment is performed with real-timePCR (qPCR), the concentration of the fluorescent dye can be increased inparticular with samples having high background (e.g. host) amounts ofDNA.

In embodiments of the methods and systems herein described wherein theamplifying the 16S rRNA recognition segment is performed with qPCR,fluorescence can be used only to prevent “over-amplification” of 16Samplicons without exact quantification of the target, according toapproaches such as the one used in a commercial microbiome 16Ssequencing library preparation kit to prevent overamplification [73,74].

In embodiments of the methods and systems herein described wherein theamplifying the 16S rRNA recognition segment is performed with qPCR,real-time amplification procedures with barcoding for next generationsequencing are well known in the art. The same amplification protocolsused by the Earth Microbiome Projects(earthermicrobiome.org/emp-standard-protocols/16s) can be used herein aswill be understood by a skilled person.

In some embodiments, amplification of the 16S rRNA recognition segmentcan be performed under real-time fluorescence measurements on areal-time PCR instrument (see Example 1, in FIG. 1, Panel C), thusenabling a single-step 16S rRNA gene copies quantification and ampliconbarcoding approach. This approach is referred to as “barcoding qPCR” or“BC-qPCR”.

Accordingly, quantitative real-time PCR data (Cq values) are recorded(see Example 1 and FIG. 1 panel D) and used to calculate the absoluteabundance of the sample 16S rRNAs in each sample based on the sample 16SrRNA standards (or anchors) included within the same BC-qPCR run (seeExample 1 and FIG. 2, panels A-B) or to calculate the absolutefold-differences in the 16S rRNA gene DNA copy load among the samples inthe absence of the standards (or anchors) (see Example 1 and FIG. 2,panel C). These data are further used to calculate the absoluteabundances or fold differences in the absolute abundances of single 16SrRNA of the sample 16S rRNA in the analyzed samples.

In some embodiment, wherein the amplifying is performed with BC-qPCR,single 16S rRNA gene DNA standard (or anchor) with known target templateconcentration can be included with a 96-well (96 PCR tube) run (seeExample 1 and FIG. 2, panel A). In some other embodiments, wherein theamplifying is performed with BC-qPCR, two or more 16S rRNA gene DNAstandards (e.g., serial dilutions) with known target templateconcentrations can be utilized in a similar manner (see Example 1 andFIG. 2, panel B). Including more than one standard will allow estimatingthe exact BC-qPCR efficiency (according to the equations 2.2 and 2.5 or2.3, 2.4, and 2.5 of FIG. 2) for any given batch of samples andreagents.

In some embodiments, wherein BC-qPCR is performed it can provide avaluable information about the fold difference in the absolute load of16S rRNA gene copies among compared samples (in a single batch andpotentially across multiple batches of samples from a single PCR run ormultiple separate PCR runs) without standards (anchors) included (seeExample 1 and FIG. 2, panel C). Such fold difference between samples canbe calculated using the equations 3.1 and 3.2 of FIG. 2 and assuming theempirical BC-qPCR efficiency of 95.0%. Such absolute fold differencevalues can then be converted to the absolute fold differences amongsamples for each individual taxon based on their relative abundancevalues obtained from the sequencing step as will be understood by askilled person upon reading of the present disclosure.

In embodiments, wherein PCR is performed by qPCR the total number ofsample 16S rRNAs can be obtained by detecting a concentration parametersuch as Cq (PCR cycle number at which a signal is detected), reactiontime, fluorescence intensity, and comparing the detected concentrationparameter with a standard calibration curve to obtain the nucleic acidconcentration value.

Several different anchoring approaches can be utilized to convert the Cqvalues obtained from the qPCR to provide the absolute abundance ofsample 16S rRNA in the samples. For example multiple uncharacterizedsamples (ideally with distant Cq values) can be quantified using thedPCR assay and used as anchoring “standards” for the batch of samples.Any of the multiple anchoring points then can be used to calculate thesample 16S rRNAs copy load in the remaining samples from the batch usingequations 2.1 and 2.6 of FIG. 2.

In addition or in the alternative, a single uncharacterized sample fromthe batch can be analyzed using e.g. a dPCR assay and thus can serve asa single anchoring “standard” sample. In this scenario all calculationsof the absolute concentrations of the remaining samples can be doneusing the equations 1.1. and 1.2 of FIG. 2 and would rely on theempirical BC-qPCR efficiency (e.g., 95.0%) to provide absolute abundancevalues of sample 16S rRNAs.

In some embodiments, the 16S rRNA gene amplification and barcoding canbe performed via two separate PCR reactions (“two-step barcoding”) as in[69, 73, 74] as will be understood by a person skilled in the art.

In some embodiments in which absolute abundance is obtained by BC-qPCR(which allows target amplification+barcoding+simultaneous quantificationbased on Cq) the “two-step barcoding” can be performed by recording thereal-time fluorescent signal during both of the real-time barcoding PCRreactions and combining for each sample a complete time series of thefluorescent signal from the fluorescent signal values obtained from thefirst and from the second PCR reactions. Combined fluorescent signaltime series can then be used to plot the fluorescence profiles(fluorescence values over the number of cycles) and calculate thecorresponding Cq values for each sample as in [77].

In some embodiments, the absolute abundance of 16S rRNAs can be providedas a direct measurement from experiments. For example, the total numberof sample 16S rRNAs can be provided by detecting a nucleic acid withdigital quantification methods.

As used herein the term, “digital” in connection amplification and/orquantification methods, indicates polynucleotides amplification methods,in which Single molecules can be isolated by dilution and individuallyamplified; each product is then separately analyzed. Exemplary digitalquantification methods comprise digital PCR (dPCR), or with correctionfor amplification efficiency by digital LAMP or digital RPA or otherdigital isothermal amplification chemistries.

In particular some embodiments herein described, the absolute abundanceof 16S rRNAs can be provided by amplifying the 16S rRNA recognitionsegment with digital polymerase chain reaction to quantitatively detectan absolute abundance of the plurality of sample 16S rRNAs.

As used herein, “digital PCR” refers to an assay that provides anend-point measurement that provides the ability to quantify nucleicacids without the use of standard curves, as is used in real-time PCR.In a typical digital PCR experiment, the sample is randomly distributedinto discrete partitions, such that some contain no nucleic acidtemplate and others contain one or more template copies. The partitionsare amplified to the terminal plateau phase of PCR (or end-point) andthen read to determine the fraction of positive partitions. If thepartitions are of uniform volume, the number of target DNA moleculespresent can be calculated from the fraction of positive end-pointreactions using Poisson statistics, according to the following equation:λ=−ln(1−p) (1) wherein λ, is the average number of target nucleic acidmolecules per replicate reaction and p is the fraction of positiveend-point reactions. From λ, together with the volume of each replicatePCR and the total number of replicates analyzed, an estimate of theabsolute target nucleic acid concentration is calculated. Digital PCRincludes a variety of formats, including droplet digital PCR, BEAMing(beads, emulsion, amplification, and magnetic), and microfluidic chips.

As a person skilled in the art will understand, digital PCR (dPCR)builds on traditional PCR amplification and fluorescent-probe-baseddetection methods to provide a sensitive absolute quantification ofnucleic acids without the need for standard curves.

In embodiments wherein dPCR is used to quantify the total number ofsample 16S rRNAs, a sample is typically split into two separate PCRreactions: one reaction is used for absolute quantification through dPCRand the second reaction is used for amplicon barcoding fornext-generation sequencing.

Accordingly, in embodiments wherein dPCR is used to quantify the totalnumber of sample 16S rRNAs, amplifying the 16S rRNA recognition segmentcomprises performing digital PCR to quantitatively detect an absoluteabundance of the plurality of sample 16S rRNAs in the sample andperforming real-time PCR to provide an amplified 16S rRNA recognitionsegment.

In some embodiments dPCR reactions are set up with 16 S rRNA gene primercontaining the target primer sequence sequences configured for thesequencing according to methods and systems herein described. The PCRreaction mix further comprises conventional commercial reagents for 16SrRNA gene amplicon library preparation as will be understood by a personskilled in the art. Reactions can be run in replicates to improvequantification precision and resolution and amplicon barcodinguniformity.

In embodiments herein described, the methods and systems of thedisclosure also comprise b) sequencing the 16S rRNA recognition segmentwith the primers comprising the target specific for the 16S rRNAsconserved region to detect a relative abundance of the target 16S rRNAwith respect to the plurality of sample 16S rRNAs in the sample,

As a person skilled in the art will understand, the sequencing inmethods and systems herein described is used to detect the order ofnucleotides present in the 16S rRNA recognition segment and todifferentiate among the sample 16S rRNAs to detect a relative abundanceof the target 16S rRNA with respect to the total 16S rRNAs in thesample. As a person skilled in the art will understand, a relativeabundance used herein is the percent composition of the target 16S rRNArelative to the total number of sample 16S rRNA in the sample.

The word “sequencing” as used herein indicates massive parallelsequencing performed via spatially separated, clonally amplifiedpolynucleotide templates or single polynucleotide molecules, as will beunderstood by a skilled person. In particular, in embodiments hereindescribed sequencing can be performed by Next Generation Sequencingperformed by generating sequencing libraries by clonal amplification ofa target polynucleotide by PCR in vitro to provide amplified templatesor providing single target polynucleotides; spatially segregating,amplified templates or single target polynucleotide; and sequencing thespatially segregated target polynucleotide by synthesis, such that thesequence is determined by the addition of nucleotides to thecomplementary strand rather than through chain-termination chemistry.While these steps are followed in most NGS platforms, each utilizes adifferent sequencing approach such as Pyrosequencing, Sequencing byreversible terminator chemistry, Sequencing-by-ligation mediated byligase enzymes, and Phospholinked Fluorescent Nucleotides or Real-timesequencing as will be understood by a skilled person. Exemplary NGS kitscommercially available include Illumina™ sequencing, Roche 454™sequencing, Ion torrent: Protein/PGM™ sequencing, Nanopore sequencing,and SOLiD™ sequencing. Next generation sequencing methods are known inthe art, and are described, e.g., in Metzker, M. Nature BiotechnologyReviews 11:31-46 (2010).

Accordingly in some embodiments sequencing is performed by performingnext-generation sequencing of a 16S rRNA comprising the recognitionsegment the amplified 16S rRNA recognition segment with the same primersdescribed herein for the PCR, which are specific for the 16S rRNAsconserved region.

In some embodiments sequencing can be performed by performing long readsequencing (Nanopore) which is a sequencing method based on measuringchanges in voltage as the bases pass through a membrane protein pore. Inthose embodiments typically up to nearly full length 16S sequence isamplified using non-barcoded primers. Amplicons are then end-repaired,dA-tailed, ligated with barcodes, and then pooled (from multiplesamples) barcoded amplicons were ligated with sequencing adapters beforesequencing. [58, 59].

In the embodiments herein described, the primers used for sequencing the16S rRNA comprise a forward and a reverse primer sequence. Inparticular, in methods and systems herein described the forward andreverse primer sequence comprise the same target primer sequences thatare specific to the 16S rRNA conserved regions of the 16S rRNArecognition segment flanking the variable region of the same 16S rRNArecognition segment.

Accordingly, both the forward and reverse primers used fornext-generation sequencing and the forward and reverse primers used inPCRs described above comprise the same target primer sequences that aresubstantially complementary to the 16S rRNA conserved regions of the 16SrRNA recognition segment.

In some embodiments, the primers used for next generation sequencingcomprise a forward primer having a sequence of 5′-CAGCMGCCGCGGTAA-3′)(SEQ ID NO: 53) and a reverse primer having a sequence of5′-GGACTACHVGGGTWTCTAAT-3′ (SEQ ID NO: 54).

Additional exemplar primers are listed in Table 2 below,

TABLE 2 Primer Orien- SEQ ID name Sequence tation NO: UN00F02.1CAGCMgCCGCGGTaA Forward 55 UN00F02.2 CAGCNgCCGCGGTaA Forward 56UN00F03.1 AGCMgCCGCGGTaA Forward 57 UN00F03.2 AGCNgCCGCGGTaA Forward 58UN00F04.1 GTGYCAGCMgCCGC Forward 59 UN00F04.2 GTGNCAGCNgCCGC Forward 60UN00F05.1 GTGYCAGCMgCCG Forward 61 UN00F05.2 GTGNCAGCNgCCG Forward 62UN00F06.1 GTGYCAGCMgCC Forward 63 UN00F06.2 GTGNCAGCNgCC Forward 64UN00F07.1 GCMgCCGCGGTaA Forward 65 UN00F07.2 GCNgCCGCGGTaA Forward 66UN00F08.1 CMgCCGCGGTaA Forward 67 UN00F08.2 CNgCCGCGGTaA Forward 68UN00F09.1 GCMgCCGCGGTa Forward 69 UN00F09.2 GCNgCCGCGGTa Forward 70UN00F10.1 AGCMgCCGCGGTa Forward 71 UN00F10.2 AGCNgCCGCGGTa Forward 72UN00R00.1 gGacTAcHVGGGTWTCTAAT Reverse 73 UN00R00.2 gGacTAcNNGGGTNTCTAATReverse 74Y, M, H, V, and W indicate degenerate bases according to the IUPACnotation;N indicates degenerate base according to IUPAC notation, or a universalbase (as described in the text of the disclosure), or a combination ofthese two (when sequence has multiple N),Lowercase letters indicate LNA modifications at a single location,various combinations of more than one location, or all locations withina sequence.

The primers shown in Table 2 can form primer pairs to be used in methodand systems herein described in any combination of forward and reverseprimers as will be understood by a skilled person. In particular,primers comprising LNA have been designed to facilitate the primerbinding to specific microbial 16S rRNA polynucleotides while reduce itsbinding to animal mitochondrial polynucleotides, as will be understoodby a skilled person upon reading of the present disclosure.

A person skilled in the art will understand, the primers used forsequencing 16S rRNAs can further comprise an index sequencing primerthat carries a reverse complement of the sequence that targets 3′ end ofthe 16S rRNA amplicon.

In some embodiments, the next-generation sequencing approach used hereinis amplicon sequencing. “Amplicon sequencing” as used herein refers to atargeted sequencing method in which a discrete region of a genome isfirst amplified from the entire genome using PCR and the generatedamplicons are used as templates for subsequent sequencing. Sequencingcan be carried out in a sample containing amplification products of asingle amplicon. Alternatively, the sample can contain mixtures ofmultiple amplicons pooled together, as will be understood by a skilledperson. Amplicon Sequencing is a method where multiple amplicons arepooled together and co-sequenced.

“Amplicons” as used herein are defined as replicated DNA (or ribonucleicacid—RNA) strands that are formed by polymerase chain reaction (PCR),ligase chain reactions (LCR), or other DNA duplication methods, wherethe strands are copies of a target region of a genome. In order tomultiplex PCR amplification, each amplicon has to be unique andindependent (no overlapping amplicons), which requires careful selectionof the primers used to tag the regions to be amplified. Amplicons forsequencing have a length typically in the range between 100 bp and 500bp.

The processing and sequencing of amplicons with different sequencingplatforms can be flexible and allows for a range of experimentaldesigns. A variety of options regarding design parameters can beselected, such as the length of amplicons, the number of ampliconspooled together, the number of reads desired for a given amplicon or apool of amplicons, whether to read from one end (unidirectionalsequencing) or both ends (bi-directional sequencing) of the amplicon andother factors identifiable to a skilled person in the art.

In some embodiments herein described, the 16S rRNA amplicon samplesgenerated from real-time PCR are quantified, pooled, purified, andsequenced on an NGS instrument. NGS sequencing results provide thesequence read and count data which enable taxa identification andcalculation of the corresponding taxa relative abundance profiles forthe analyzed samples (“E” in FIG. 1).

The terms “read” or “reads” used herein are defined as a sequenced rangeof DNA or RNA. A read can be a sequence that is output by a sequencinginstrument, where the read attempts to match a range of DNA that wasinput to the instrument. Each set of reads maps to a particularamplicon, with a read being a sequence for the complete amplicon or,typically, a range of bases comprising a subset of the amplicon. Thetotal set of reads in the input data for the filter pipeline can includemultiple amplicons, each having multiple reads mapped to them. The rangeof the read lengths depends upon the primers chosen for a given library.The mapping of reads to an amplicon can be determined by overlappingpaired-end reads (generally shorter than the length of the amplicon) foreach sequenced amplicon to obtain the complete 16S amplicon sequences.Complete 16S amplicon sequences are used in downstream analysis toidentify their proportions in the entire 16S amplicon pool and toidentify their taxonomic origin. The mapping of reads to an amplicon canalso be determined during alignment/assembly using a sequencingalignment tool, for example the Bowtie™ 2 read alignment tool from JohnsHopkins University (see “Fast gapped-read alignment with Bowtie 2” byBen Langmead and Steven L. Saizberg, Nat Methods, Author manuscript; PMC2013 Apr. 1).

In some embodiments, the complete 16S amplicon sequences can be obtainedby combining overlapping paired-end reads (generally shorter than thelength of the amplicon) for each sequenced amplicon to obtain thecomplete 16S amplicon sequences. Complete 16S amplicon sequences canthen be used in downstream analyses to identify their proportions in theentire 16S amplicon pool and to identify their taxonomic origin as willbe understood by a skilled person.

In some embodiments, a one-step quantification can be performed bydetecting the absolute abundance of the plurality of sample 16S rRNA byqPCR with either with one-step or two-step barcoding as describedherein, and by monitoring a library barcoding reaction fluorescence inreal-time like qPCR. The Cq of each sample can be used with a standardcurve or a defined number of standards (or anchors: multiple, two, oronly one) to determine the absolute abundance of each sample or can beused without any standards (anchors) to calculate an absolutefold-difference.

In some embodiments, a two-step quantification can be performed can beperformed by detecting the absolute abundance of the plurality of sample16S rRNA by dPCR or digital LAMP or digital RPA with non-barcodedprimers in a portion of the sample, and by detecting the relativeabundance by sequencing in another portion of the sample used to preparethe barcoded sequencing library.

In both the one-step quantification and two step quantificationembodiments according to the instant disclosure, barcoded libraries aretypically sequenced and the relative abundance of the target 16S rRNA(and/or target taxon) in the sample detected through sequencing is thenmultiplied by the absolute abundance of the plurality of sample 16S rRNA(and/or sample taxon).

In some embodiments of the methods and systems herein described thereads from the sequencing can be used to determine relative abundancesof a target 16S rRNA.

In particular, the relative abundances of each target 16S rRNA can bedetermined by dividing the number of read counts associated with eachtarget 16S rRNA or 16S rRNA of each taxon by the total read counts ofthe 16S rRNAs in the sample. Methods for analyzing amplicon sequencesfrom the next generation sequencing are well known in the art.Open-source bioinformatic platforms for such analysis include MOTHUR[78] and QIIME [79] and others identifiable to a person skilled in theart. For example, QIIME2 can be used with the DADA2 package for ASVdetermination as will be understood by a person skilled in the art [3,80].

In embodiments, where the 16SrRNA is a polyribonucleotide aftergeneration of cDNA via the reverse transcription step all sequencingmethodologies described for DNA sequencing are applicable and identicalfor RNA.

In embodiments herein described absolute abundance values of sample 16SrRNAs can be converted to the absolute abundances of target 16S rRNAbased on the target 16S rRNA relative abundance values for the obtainedfrom the sequencing.

In particular in methods and systems of the disclosure by c) multiplyingthe relative abundance of the target 16S rRNA in the sample times theabsolute abundance of the plurality of sample 16S rRNAs in the sample,to quantify the absolute abundance of the target 16S rRNA in the sample.

Accordingly, in some embodiments, the absolute abundance of the target16S rRNA can be obtained by multiplying the relative abundance of thetarget 16S rRNA in the sample from the sequencing times absoluteabundance of the plurality of 16S rRNAs in the sample obtained fromdigital PCR or qPCR (see Example 1 and FIG. 1 panels E and F).

In some embodiments, method and a system to quantify a target 16S rRNAin a sample further comprises extracting nucleic acids from the sampleto provide nucleic acids extracted from the sample for the amplifyingstep. As a person skilled in the art will understand, the extractionprocess generally comprises mechanical lysis via bead beating, capturingnucleic acids either on a silica column or magnetic beads, purifyingnucleic acids by washing with ethanol, and eluting nucleic acids off ofcolumn or beads with water.

In some embodiments, mechanical lysis can be supplemented/enhanced orsubstituted with chemical lysis (e.g., phenol/chloroform, etc.). Nucleicacids can be also precipitated or phase-separated without the use of acolumn. Washing can be done with ethanol and many other solventsElution/dissolution of washed nucleic acid can be done with water ormany different stabilizing buffers (e.g., TE buffer).

In some embodiments, the nucleic acids extracted from the sample can bea total DNA extracted and purified from samples such as feces,gastrointestinal contents or aspirates, intestinal mucosa biopsy, usingcommercially-available kits (see Example 1 and FIG. 1 panel (“A”)validated for uniform DNA extraction from complex microbiota (e.g.,ZymoBIOMICS) and for quantitative recovery of microbial DNA from sampleswith microbial loads across multiple orders or magnitude as will beunderstood by a person skilled in the art (see for example FIG. 6).

In some embodiments, the amplifying and/or the sequencing can beperformed on extracted polynucleotides from the sample. Preferablyinformation the amount of matter the nucleic acids were extracted fromis taken into account, in order to provide magnitude of the absolutequantification per sample mass/volume In particular mass or volume ofthe sample being extracted can be recorded and the quantified number of16S rRNA copies can be normalized to the recorded input mass or volumeas will be understood by a skilled person.

In some embodiments herein described the 16S rRNA primers can beoptimized to be specific for a plurality of sample 16S rRNA based on theshared conserved sequences which can be also associated with a specifictaxon grouping different prokaryotes as will be understood by a skilledperson.

In those embodiments the target 16S rRNA can be the 16S rRNA of aprokaryote of a target taxon, and the plurality of sample 16S rRNA isassociated to a sample taxon, the target taxon having a taxonomic ranklower than a sample taxon in a same taxonomic hierarchy.

In those embodiments, the absolute abundance of the target 16S rRNA canbe converted into the absolute abundance of the target prokaryotes ofthe target taxon if the 16S gene copy number in that prokaryote isknown.

In those embodiments, method and a system herein described can beperformed to quantify in a sample a target prokaryote of a target taxon,the target taxon having a taxonomic rank lower than a sample taxon in asame taxonomic hierarchy.

In those embodiments the method comprises amplifying a 16S rRNArecognition segment comprising a 16S rRNA variable region specific forthe target taxon flanked by 16S rRNA conserved regions specific for thesample taxon, by performing polymerase chain reaction on nucleic acidsextracted from the sample with primers specific for the 16S rRNAconserved regions to quantitatively detect an absolute abundance ofprokaryotes of the sample taxon in the sample.

In some embodiments of methods and systems to quantify in a sample aprokaryote of a target taxon within a sample taxon, the 16S rRNArecognition segment herein described can comprise a 16S rRNA variableregion specific for a phyla (a target taxon) in the prokaryotic bacteriasuch as Bacillales, Lactobacillales, Clostridiales, Pseudomonadales, andother phyla, in which the 16S rRNA variable region is flanked by 16SrRNA conserved regions specific all the prokaryotic bacteria in thesample.

In some embodiments of methods and systems to quantify in a sample aprokaryote of a target taxon within a sample taxon, the 16S rRNArecognition segment herein described can comprise a 16S rRNA variableregion specific for a species (a target taxon) which belongs to a genus(a sample taxon), in which the 16S rRNA variable region is flanked by16S rRNA conserved regions specific for that genus.

In some embodiments of methods and systems to quantify in a sample aprokaryote of a target taxon within a sample taxon, the 16S rRNArecognition segment herein described can comprise a 16S rRNA variableregion specific for a genus (a target taxon) which belongs to a family(a sample taxon), in which the 16S rRNA variable region is flanked by16S rRNA conserved regions specific for that family.

In some embodiments of methods and systems to quantify in a sample aprokaryote of a target taxon within a sample taxon, the 16S rRNArecognition segment herein described can located in the V4 region of the16S rRNA gene.

Accordingly, in some embodiments of methods and systems to quantify in asample a prokaryote of a target taxon within a sample taxon the 16S rRNAprimers used herein comprise a target primer sequence specific to theconserved regions in the V4 region of the microbial 16S rRNA gene.

In some embodiments of methods and systems to quantify in a sample aprokaryote of a target taxon within a sample taxon, the target primersequence of the 16S rRNA primers used can be an optimized version of theoriginal EMP primer set in which the EMP forward primer at its 5′ end isredesigned to start at the position 519 of the V4 region of microbial16S rRNA gene sequence (FIG. 3, panel A).

In some embodiments of methods and systems to quantify in a sample aprokaryote of a target taxon within a sample taxon the 16S rRNA primerscan be optimized by redesigning the EMP forward primer so that thenonspecific annealing to the host rRNA such as the mouse and humanmitochondrial 12S rRNA gene DNA will be reduced or eliminated, which isthe main competing template of mammalian origin identified by ampliconsequencing of PCR products obtained with mouse germ-free tissue DNA.Such change increases the primer's specificity for low copy numbermicrobial templates in samples with high content of mouse or human hostDNA background.

Embodiments of the methods according to the disclosure, allow to performan absolute quantification of a target 16S rRNA and/or of a targetprokaryotic taxon herein described with accuracy, precision and/orresolution.

The term “accuracy” and “accurate” as used herein indicates a measure ofthe difference between a detected value and true value of a parameter.Accuracy of a method of quantification of a nucleic acid in accordancewith the instant disclosure can be determined by detecting a differencebetween quantification of the nucleic acid performed with the method andthe quantification obtained via primer specific digital PCR An accuratemethod can be one with at least 2× accuracy, with 95% confidence, formost taxa in a sample.

The term “precision” as used herein indicates a measure of thedifference between detected values of a parameter among differentmeasurements on a same reference item. Therefore precision is a measureof how similar multiple measured values (performed on the same exactsample) are to one another as will be understood by a skilled person. inorder to have a precise method at least 50% coefficient of variationshould be achieved for samples within the limits of quantification. Theterm “resolution” as used herein indicates a measure of the differencebetween detected values of a parameter between two reference items.Accordingly, resolution of a measurement indicates how far apart twosamples measured values must be to know that they are truly differentvalues.

In particular, embodiments of the methods according to the disclosure,allow to perform an absolute quantification of target 16S rRNA and/or ofa target prokaryotic taxon herein described with an increased accuracy,precision and/or resolution with respect to know methods. This is inview of performance on a same 16S rRNA recognition segment of the

detection of the absolute abundance of the a plurality of sample 16SrRNAs and/or of the sample prokaryotic taxon performed by the amplifyingand

detection of the relative abundance of the target 16S rRNA and/or of atarget prokaryotic performed by the sequencing.

In this connection, a skilled person will understand that multiplicationof the absolute abundance and relative abundance so detected allowobtaining the absolute quantification in accordance with method andsystems of the disclosure which will have an increased accuracy,precision and/or resolution with respect to known methods.

In methods and systems to provide a customized rodent model hereindescribed

amplifying a 16S rRNA recognition segment comprising a 16S rRNA variableregion specific for the target taxon flanked by target 16S rRNAconserved regions specific for the sample taxon, by performingamplification of nucleic acids extracted from the sample with primerscomprising a primer target sequence specific for the target 16S rRNAconserved regions to quantitatively detect an absolute abundance ofprokaryotes of the sample taxon in the sample and to provide anamplified 16S rRNA recognition segment,

sequencing the amplified 16S rRNA recognition segment with primerscomprising the primer target sequence specific for the target 16S rRNAconserved region and the 16S rRNA variable regions to detect a relativeabundance of the prokaryotes of the target taxon with respect to theprokaryotes of the sample taxon in the sample; and

multiplying the relative abundance of the prokaryotes of the targettaxon in the sample times absolute abundance of the prokaryotes of thesample taxon in the sample to quantify the absolute abundance of theprokaryotes of the target taxon in the sample,

are performed in connection with the detection of in one or more targetprokaryotes and related presence, proportion of and total load in asample obtained from the rodent.

In particular, in preferred embodiments, the absolute abundance ofdetected target prokaryote of detected taxa is typically normalized tothe input sample weight into extraction.

In some embodiments, the method can further comprise separating (e.g.through filtering out) low abundant likely contaminant taxa beforedetection of presence/absence and/or analysis if desired.

In the method to provide a rodent model herein described, the detectedpresence of the target prokaryote in the rodent is provided by thedetected relative abundance and/or absolute abundance wherein presenceis detected when the relative abundance is greater than a threshold.

In some embodiments, the threshold for determination of the detectedpresence or absence of the target prokaryote, can be determined in viewof the experimental consideration such as limit of detection of theabsolute quantification method. For example, the limit of detection canbe determined via Poisson statistics. Accordingly, one can define aconfidence level (a common one is to use a 95% confidence interval butlower confidence can be used), and a total load can be used to determinethe limit of detection. For example, if a sample has a total load of 100copies of a prokaryote then Poisson statistics indicates that to have a95% confidence of detection one can measure a prokaryote that has 3copies. A taxon with 3 copies out of 100 copies has a relative abundanceof 3%. Therefore, the threshold for this sample would be 3%.

In some embodiments, the threshold for determination of the detectedpresence or absence of the target prokaryote, can be arbitrary.

In some embodiments, the threshold for determination of the detectedpresence or absence of the target prokaryote, can be zero.

In the method to provide a rodent model herein described, the detectedproportion is provided by the relative abundance of the targetprokaryote and/or by the quantified absolute abundance in combinationwith the detected relative abundance providing the detected proportionof the target prokaryote in the rodent.

In the method to provide a rodent model herein described the total loadof the target prokaryote is provided by detected absolute abundance ofthe target prokaryote of the target taxa and/or by the detected absoluteabundance of prokaryotes of the sample taxon as will be understood by askilled person.

In some embodiments, herein described the load of all taxa can bedetermined by the absolute abundance determined from amplification andquantification of the 16S rRNA gene segment through an absoluteabundance of prokaryotes of the sample taxon or through combination ofdetected absolute abundances of more than one target prokaryotes.

In preferred embodiments, the target microbiome can be the microbiome ofone individual (herein individual microbiome) and the target presence,target proportion and/or target total load of the one or more targetprokaryotes forming part of the target microbiome profile is provided bythe presence, proportion and/or target load of the one or more targetprokaryotes detected in the microbiome of the individual.

In some preferred embodiments, wherein the target microbiome is anindividual microbiome, the method can further comprise determining theindividual microbiome profile by absolute quantification method.

In those embodiments, the method can further comprise quantifyingabsolute abundance of the target prokaryotes in a sample from theindividual comprising the individual microbiome. In particular, thequantifying is preferably performed by the amplifying, the sequencingand the multiplying herein described on an individual sample comprisingthe individual microbiome to provide absolute quantification of thetarget prokaryotes in the individual sample, and to further provide adetected presence, detected proportion and/or detected total load of thetarget prokaryotes in the sample of the individual based on the relativeabundance and/or absolute abundance so obtained.

The detected presence, detected proportion and/or detected total load ofthe target prokaryotes in in the sample of the individual can provide orbe used to provide following a plurality of measurements) the individualpresence, individual proportion and/or the individual total load of thetarget prokaryotes forming or comprised in the individual microbiomeprofile.

In further preferred embodiments, the target microbiome can be a targetmicrobiome obtained by averaging the individual microbiome of aplurality of individuals (herein target averaged microbiome), and thetarget presence, target proportion and/or target total load of the oneor more target prokaryotes forming part of the target microbiomeprofile, is provided by the averaged presence, averaged proportionand/or averaged target load of the one or more target prokaryotesdetected in the microbiome of each individual of the plurality ofindividuals.

In those embodiments, the method can further comprise determining anindividual microbiome in each individual of the plurality of theindividuals, by quantifying absolute abundance of the target prokaryotesin a sample from each individual of the plurality of the individuals thesample comprising the target microbiome, according to methods andsystems herein described. The individual presence, individual proportionand/or the individual total load of the target prokaryotes forming partof the individual microbiome profile of each individual of the pluralityof individuals are then averaged to provide the averaged presence,averaged proportion and/or averaged target load of the target averagedmicrobiome.

In methods and systems to provide a rodent, the method further comprisescomparing the detected presence, the detected proportion of and/or thedetected total load of the target prokaryotes with the target presence,the target proportion and/or the target total load of the targetprokaryotes in the target microbiome profile to detect differencesbetween

the detected presence, detected proportion of and/or the detected totalload of the target prokaryotes with the target presence, the targetproportion and/or the target total load of the target prokaryotes and

the target presence, the target proportion and/or the target total loadof the target prokaryotes, to obtain the rodent model having the targetmicrobiome profile.

Accordingly, the detected microbiome profile and the target microbiomeprofile can be considered substantially the same of when the detectedpresence, proportion and/or total load of target prokaryote andcorresponding target presence, proportion and/or total load of targetprokaryote are substantially the same.

The term “substantially same” between two values indicates that thedifference between these two values is below a threshold. The thresholdcan either be an arbitrary value or a value defined based on the limitof detection methods and/or factoring in the related biological and/ortechnical variability. The threshold can be determined in view ofstatistical considerations to reduce or minimize inaccurate detectionsuch as the application of Poisson statistics as will be understood by aperson skilled in the art. For example, the threshold can be definedaccording to a confidence interval which is typically selected to be 95%or lower. In some embodiments, the threshold can be an arbitrary valuesuch as 0.01% relative abundance. In some embodiments, the threshold canbe zero.

In particular, in embodiments herein described, since presence,proportion and total load of target prokaryotes is provided by theirrelative and/or absolute abundance, the comparing is performed bycomparing

the detected relative abundances and/or absolute abundance providing thedetected presence detected proportion and/or detected total load of thetarget prokaryote,

with corresponding

target relative abundances and/or absolute abundance providing thetarget presence target proportion and/or target total load of the targetprokaryote

to establish whether they are substantially the same.

In particular, in some embodiments, the comparing between two microbiomeprofiles, comprises comparing a total microbe load of a detectedmicrobiome to a total microbe load of a target microbiome and comparinga relative abundance of each taxon in the detected microbiome to arelative abundance of the same target prokaryotes in the targetmicrobiome.

In some embodiments, the comparing between two microbiome profilescomprises comparing the detected relative abundance of each targetprokaryote in the detected microbiome to a relative abundance of thesame target prokaryote in the target microbiome.

In some embodiments, the comparing between two microbiome profilescomprises comparing a total load of target prokaryote in an absoluteabundance of each target prokaryote in the detected microbiome to anabsolute abundance of the same target prokaryote in the targetmicrobiome.

A person skilled in the art will understand, a relative abundance of atarget prokaryote (or taxon) in a sample is a ratio of an absoluteabundance of the taxon in the sample to the total microbe load of thesample. In particular, to detect whether a target prokaryote is presentin a microbiome or not, one can determine the presence of the targetprokaryote by detecting if the relative abundance of the targetprokaryote is greater than zero or an arbitrary threshold value asdescribed herein.

In some embodiments, methods to compare two microbiome profiles can beperformed using beta-diversity metrics. Common beta-diversity metricsinclude bray-curtis, Euclidean, jaccard, unweighted unifrac, andweighted unifrac. When using these metrics they commonly scale from 0-1where 0 means completely different and 1 means identical. In general,one can compare two profiles by first measuring both with thequantitative sequencing method herein described, then comparing the twoprofiles with one of the beta-diversity metrics, then determining ifthey are substantially the same by seeing if the metric is greater thanan arbitrary threshold.

In some embodiments, the method to provide a rodent model according tothe disclosure can optionally further comprise

modifying the rodent microbiome by introducing, enriching and/ordepleting prokaryotes in the rodent microbiome to provide the rodentmicrobiome with the target prokaryotes with the target presence, thetarget proportion, and/or the target total load;

obtaining a sample of the rodent comprising the rodent microbiomefollowing the modifying to obtain a rodent modified sample;

quantifying absolute abundance of the target prokaryotes in the rodentmodified sample, to obtain a detected rodent modified presence, adetected rodent modified proportion and/or a detected rodent modifiedtotal load of the target prokaryotes in the rodent, each targetprokaryote being of a target taxon having a taxonomic rank lower than asample taxon in a same taxonomic hierarchy; and

comparing the detected rodent modified presence, the detected rodentmodified proportion and/or the detected rodent modified total load ofthe target prokaryotes with the target presence, the target proportionand/or the target total load of the target prokaryotes in the targetmicrobiome profile.

Those embodiments are preferably performed when the detected presence,proportion and/or total load of the target prokaryote, are notsubstantially the same, and more preferably substantially different fromcorresponding target presence, proportion and/or total load of thetarget prokaryote of the target microbiome.

The term “substantially different” between two values indicates that thedifference between these two values is statistically significant.Statistical significance indicates that the difference between these twovalues is statically significant over the related biological and/ortechnical variability. A difference has statistical significance when itis very unlikely to have occurred given the null hypothesis which is adefault hypothesis that a quantity (herein difference) to be measure iszero as will be understood by a person skilled in the art. Statisticalanalysis of the data comprises testing the null hypothesis as a skilledperson would understand and then reject the null hypothesis if theprobability of generating the observed data is less than the p-value, athreshold for statistical significance chosen by a practitioneraccording the standards of the field. Common choices for the p-valuewould be 0.05, 0.025, and 0.01. Additional description of statisticalanalysis used in single-molecule (digital) measurements to resolvedifferences between two distributions can be found in publishedliteratures such as in Kreutz et al 2011[81].

In those embodiments, and in general any time it is desired in view ofexperimental design, the method to provide a rodent model according tothe disclosure can optionally further comprise

repeating the modifying, the obtaining to provide a rodent modifiedsample and the quantifying absolute abundance of the target prokaryotesin the rodent modified sample until the detected rodent modifiedpresence, the detected rodent modified proportion and/or the detectedrodent modified total load of the target prokaryote is substantially thesame of the target presence, the target proportion and/or the targettotal load of the target prokaryotes, to obtain the rodent model havingthe target microbiome profile.

Methods and systems of the present disclosure directed to modify arodent microbiome to provide a rodent model having a target microbiome,are based on modifying the rodent microbiome, by introducing targetprokaryotes or prokaryotes nutrients in the rodent microbiome andquantifying absolute abundance of target prokaryotes of the targetmicrobiome with absolute quantification methods and systems hereindescribed to detect presence, proportion and/or total load of one ormore target prokaryotes of the target microbiome in the rodent.

In some embodiments the modifying is preceded by quantifying absoluteabundance of rodent prokaryotes of the target microbiome with absolutequantification methods and system herein described to detect presence,proportion and/or total load of one or more rodent prokaryotes of therodent microbiome in the rodent.

In embodiments of methods and systems to provide a customized rodentmodel herein described, the modifying can be performed with any methodsdirected to introduce, enrich and/or deplete prokaryotes of the rodentmicrobiome to provide the rodent microbiome with the target prokaryoteswith the target presence, the target proportion and/or the target totalload.

In some embodiments, the modifying can comprise introducing prokaryotesand in particular target prokaryote, and/or introducing prokaryotes'nutrients in the rodent microbiome to be modified.

In some embodiments, prokaryotes can be introduced into the rodenteither via a single, repeated, or continuous administration.

In some embodiments, prokaryotes can be introduced into the rodent viaone or more of the following routes supplementation into the diet ordrinking water, application onto the body/fur followed by the rodentgrooming, application onto the rodent cage parts or bedding, temporaryco-housing with a pre-colonized rodent of the same species withpermitted allo-coprophagy (between the animals), oral gavage orintrarectal administration.

In some embodiments, the introducing can be performed by providing apure or mixed culture of a target prokaryote, possibly by growing theculture in laboratory and introducing the culture in the rodentmicrobiome.

In some embodiments, prokaryotes can be introduced into the rodent in avegetative or spore form.

In some embodiments, prokaryotes can be obtained either from an in vitrocell culture or from an in vivo (“raw”) sample (e.g., human saliva,gastrointestinal contents, stool, mucosal biopsy, gastrointestinallavage fluid/aspirate, mucosal wash, or any other human-associatedsample type).

In some embodiments, prokaryotes can be introduced into the rodenteither as single species/strains or as a part of complex microbialmixture.

In some embodiments, prokaryotes can be introduced into the rodenteither via a single administration, repeated, or continuousadministration.

For example, in preferred embodiment, where the target microbiome is aGIT microbiome of a human individual introducing prokaryotes: can beperformed by obtaining a sample from the gastrointestinal tract of ahuman modifying the sample to provide the same in an administrable formsuch as a slurry with a buffer (e.g., PBS) and then introducing thesample in the administrable form to the rodent animal through gavage orenema[82]. In particular, in this embodiment the sample is typicallyfrom the gastrointestinal organ one wants to model in the rodent.

In some embodiments, the modifying comprises direct or indirectenrichment or depletion of prokaryotes by administering the rodents withagents influencing the prokaryote growth and physiology, such asprokaryotes nutrients or other agents identifiable by a skilled person.

In some embodiments the modifying can comprise administering nutrientsto the rodent through a route allowing contacting of the route with therodent microbiome wherein the nutrients are selected to increase thepresence, proportion and/or total load of target prokaryotes and/or toreduce the presence, proportion and/or total load of prokaryotes otherthan target prokaryotes.

In particular, in some embodiments, nutrients to be introduced in therodent microbiome can be formulated into pellets, powder, or paste andfed to the rodent, and if water soluble, dissolved in the drinking waterand given to the rodent and/or administered through oral gavage orenema.

In preferred embodiment, where the target microbiome is a GIT microbiomeof a human individual administering prokaryotes nutrients to the rodentcan be performed by orally and/or rectally administering nutrients tothe rodent, to modify microbiome composition of the rodent GITmicrobiome.

In some embodiments, the administering can comprise providing the rodentwith modified diets (e.g. with adjusted ratios of macronutrients, suchas proteins, carbohydrates, and fats), dietary compounds (e.g.,vitamins, dietary fiber or simple carbohydrates non-digestible by therodents), or xenobiotics (non-antimicrobial drugs or chemicalcompounds).

In some embodiments, the specific diets can be administered in variousforms of enteral nutrition, such as: pellets, powder, pastes, solutionor liquid.

In some embodiments, the administering the specific diets can beadministered in various modes, such a “ad libitum”: the rodent hasaccess to the unlimited amounts of diet at all times; “timed-controlled”(e.g., timed feeding, intermittent feeding, intermittent fasting): therodent has access to the diet regulated over time; “amount-controlled”:the rodent has an access to a predefined amount of diet per unit oftime; and/or “forced”: the rodent is administered with a predefinedamount of diet (e.g., liquid) via forced feeding (gavage) at apredefined schedule.

In some embodiments, specific diets can be administered in the form ofparenteral nutrition. The diet can also be administered to the rodent asa sterile solution via a repeated or continuous injection.

In some embodiments, the modifying comprises direct or indirectenrichment or depletion of prokaryotes by administering the rodents withagents influencing the prokaryote growth and physiology.

In some of embodiments, the administering the rodents with agentsinfluencing the prokaryote growth and physiology, can comprise settingthe oxygen levels in the rodent (and consequently in the GIT) by meansof exposure of the rodent to hypoxic/hypobaric or hyperoxic/hyperbaricatmosphere.

In some of embodiments, the administering the rodents with agentsinfluencing the prokaryote growth and physiology, can comprisemanipulating the prokaryote enzymatic activity via administration ofnon-antimicrobial drugs/compounds (e.g. inhibitors ofbeta-glucuronidases or bile salt hydrolases).

In some of embodiments, the administering the rodents with agentsinfluencing the prokaryote growth and physiology, can compriseadministering agent directed to obtain targeted or broad-spectrumenrichment and/or elimination of prokaryotes: For example, in someembodiments, the agent can comprise an antimicrobial agents with orwithout non-antimicrobial agents known to amplify or reduce theantimicrobial effects of the antimicrobial agents. In those embodimentsantimicrobial agents can be delivered as individual compounds or in theform of mixtures in some embodiments the antimicrobial agent comprisesone or more prokaryotic viruses (phages).

In some embodiments, the modifying comprises direct or indirectenrichment or depletion of prokaryotes by manipulating the rodent'sphysiology to modify the rodent microbiome composition.

For example, in preferred embodiment wherein the rodent microbioate isthe GIT microbiota, the modifying can comprise one or more of

administering to the rodent of pharmaceutical agents decreasing (e.g.,loperamide) or increasing (e.g., metoclopramide, domperidone) the GITmotility;

administering to the rodent pharmaceutical agents decreasing (e.g.,omeprasol, ranitidine) or increasing (e.g., linaclotide, lubiprostone)the secretory function;

administering to the rodent agents impacting the rodentsdigestive/enzymatic and absorptive function resulting in alteredretention of macronutrients in the GIT (e.g. acarbose, orlistat);

administering to the rodent exogenous bile acids or administration ofbile acids sequestrants (e.g., cholestyramine, colestipol, colesevelam),to modify the bile acid levels in the rodent's gastrointestinal tract;

administering viruses (e.g., LP-BM5 murine leukemia virus (MuLV)), drugs(e.g., cytostatic/anticancer drugs, steroid immunosuppressants,antibodies (e.g., anti-CD4 or anti-CD8 antibodies), or ionizingradiation to modify the rodent's immune status and function; and

surgically modifying the rodent GIT, such as: bile duct ligation,pancreatic duct ligation, resection and/or anastomoses of the GITsegments, creation of blind loops.

In preferred embodiment, the microbiome modeled in the customized rodentmodel of the present disclosure is a GIT microbiome and modifying therodent microbiome comprises preventing coprophagia of the rodent.

A main purpose of preventing coprophagy is to eliminate re-introductionof fecal (large intestine) microbiota into the upper gastrointestinaltract (mouth, pharynx, esophagus, stomach, small intestine) of rodentsas will be understood by a skilled person.

A further the main purpose of preventing coprophagy is tocompartmentalize the upper gastrointestinal microbiota fromcontamination with the fecal (large intestine) microbiota.

Accordingly, preventing coprophagia allows compartmentalization of theupper gastrointestinal microbiota of the rodent and therefore provides arodent model with true GI compartment-specific microbiota with its GIcompartment-specific function present in each individual sequentialsegment of the GIT.

Accordingly preventing coprophagia as used herein encompass anyprocedure directed to reduce, minimize or eliminate re-introduction offecal microbiota into the upper gastrointestinal tract of rodents aswill be understood by a skilled person, and/or to compartmentalize theupper gastrointestinal microbiota from contamination with the fecal(large-intestine) microbiota.

Preventing coprophagia can comprise using of known devices, methods andsystem such as tail cups devices, Elizabethan collars device, or rodentjackets can be affixed to the rodent throughout a set duration.

Preventing coprophagy can also be performed by devices methods andsystems such as restraining cages (e.g., tubular or circular cage) withthe dimensions not allowing the rodent to reach its hind end and accesstheir fecal excretions (such as those described in Ref.[83-86], Wearablerestraining devices such as those described in Ref. [87], Wearablejackets: such as those described in Ref. [86, 88], Wearable Elizabethancollars in combination with wire/grid/mesh floors[89], Wearabletail/anal cups: such as those described in Ref. [90] [86, 91-101].

If multiple rodents are used concurrently, all rodents are mostpreferably be single-housed to prevent one rodent from eating anotherrodent's feces in.

A preferred device for preventing coprophagia in a rodent is provided bya tail cup device of the present disclosure. An additional descriptionis provided with reference to FIGS. 45-48 showing an exemplary a tailcup device (100) for animals such as rodents, the device comprised of acup (110) for trapping excreted feces and a tail sleeve (120) formounting of the cup (110) at a tail base of a rodent having asufficiently long tail, such as mice (including deer mouse, Natalmultimammate mouse, vesper mouse, long-tailed pocket mouse, littlepocket mouse, canyon mouse, members of the genus harvest mouse) or rats(including cotton rat, obese sand rat, rice rat, white-tailed rat,kangaroo rat, desert woodrat), degu, voles (bank, red-backed vole,meadow vole, mountain vole, tundra vole, prairie vole, woodland/pinevole, Brandt's vole, California vole), gophers (e.g., pocket gophers),mole-rats (e.g., naked mole-rat, Damaraland mole-rat), and moles, thetail sleeve (120) being configured to be applied to the tail of therodent.

In the illustration of FIGS. 45-48, the cup (110) is a tubular-shapedcomponent configured to trap fecal matter and prevent the rodent fromaccessing it, with an exemplary length of 2-5 cm and an exemplarydiameter of 1-3 cm. While FIGS. 45-48, show a circular or oval crosssection by way of example, any other shape, e.g. rectangular, can bedevised by the person skilled in the art. Additionally, while thedrawings show a generally uniform cross-section of the cup along itslength, embodiments are also possible where larger cross-section areasare provided close to one end of the cup (110) and smaller cross-sectionareas are provided close to the other end of the cup (110).

In the illustration of FIGS. 45-48, the distal end or surface (130) ofthe tubular cup (110) comprises an orifice (140) operating as a lockingopening of the cup (110) to allow passing through of the tail sleeve(120) from the inside to the outside of the cup (110). The orifice (140)may be round, oval or of similar shape with an exemplary 0.5-0.7 cmdiameter. In order to allow a proper locking engagement of the tailsleeve (120) when applied to the tail of the rodent and when pressure isnot applied to the sides of the cup (110), the diameter (or at least oneof the two axes) of the orifice (140) is smaller than the diameter ofthe tail sleeve (120). While the figures show a central placement of theorifice (140) on the distal surface (130), off-center placements arealso possible. Off-center placement of the orifice, for example closerto the dorsal side of the cup, would allow for an increased size(centrally asymmetric on the cross-section and linearly asymmetric on alongitudinal section) cup compartment on the ventral side of the cupwhere fecal matter may accumulate under gravity when animals spend mostof their time in their natural prone position.

In the illustration of FIGS. 45-48, distal surface (130) also includesan unlocking slit (150A-D) for opening of the cup (110) before or afteruse. Unlocking slit (150A-D) has a narrow diameter when compared withother dimensions of the cup (110), usually less than 1.0 mm. While theunlocking slit includes portions (150A), (150B) across the orifice (140)on the distal surface (130), intersecting the orifice (140) and spanningalong a diametral extension of the distal surface (130), it alsoincludes side portions (150C), (150D) extending along opposite sidewalls of the cup (110). Other embodiments can also be provided (e.g. incase the locking orifice is placed off-center) where the unlocking slitcrosses the orifice along a chord extension (for circular devices) ofthe distal surface. The purpose of the unlocking slit is toinstall/unlock the tail sleeve (120) in/from the orifice (140) byincreasing the gap formed by the orifice (140) through pression (e.g.with fingers, such as thumb on one side and index on the other whileholding the cup) alongside portions (150C), (150D), e.g. on pressingpoints (150CC) and (150DD), corresponding to the ends of theirrespective side portions. These pressing points can have no specificshape at all and just be located at the straight end of their respectiveside portions, or can have a shape (e.g. circular with a 1-2 mmdiameter) to address deformation stress dissipation concerns uponenlargement of the orifice. If desired, as also shown in the drawings,the pressing points (150CC), (150DD), can be placed on opposite sides ofthe cup (110) (e.g. 180 degrees apart in case of a cylindricalembodiment) to allow for a stronger hold of the cup (110) while applyingpressure, thus providing better structural integrity and responsivenessto the pressing force. The length of side portions (150), (150D) dependson parameters such as cup length, shape, cross section profile, size andmaterial and should be chosen to allow a sufficient increase of theorifice and unlocking slit when unlocking the cup (110) from the tailsleeve (120) upon application on the pressing points (150CC), (150DD) toallow removal of the cup (110) from the rodent and/or related emptyingof the cup (110), while not compromising the mechanical integrity of thedevice, not increasing the risk of the locking mechanism failure, andthe cup's purpose to effectively entrap fecal matter. While the figuresexemplary show a flat arrangement of the distal surface (130), suchsurface can also be spherical, conical or differently shaped, ifrequired.

In the illustration of FIGS. 45-48, reference will now be made to theproximal end or surface (160) of the cup (110), the proximal end havinga cross sectional dimension sufficiently wide to fit around a posteriorend of the rodent more proximal than the anus to ensure falling of thefecal pellets into the cup, but also preferably more distal than theurethral opening and genitals to prevent urine from accessing the cupand from discomfort or damage to the genital area of the animal. Whilethe proximal surface (160) can be shaped as a straight/flat cut as shownin the figures, embodiments are also possible where the proximal surfaceis carved or shaped to better fit around the rodent's posterior end andbetter accommodate for the genital anatomy of the rodent, varyingbetween genders.

In the illustration of FIGS. 45-48, a reinforcement and/or protectivering (170) is located along the proximal end (160) and is configured tocome in contact with a body (skin) portion close to and/or around thegenital area of the rodent, which portion the rodent may be able toreach with its mouth and/or teeth. The reinforcement and/or protectivering (170) is made from an inert (in order not to corrode or leak anychemical compounds) material hard enough to prevent the animal fromdamaging it by chewing (which would necessitate cup replacement), suchas metal (e.g. medical grade stainless steel, titanium and/or suitablemetal alloys), ceramic, glass, tough plastic (such as PTFE/Teflon orKevlar) and/or combinations of the same. Usage of soft materials wouldlikely result in deterioration of the proximal end of the cup due torodents chewing on it, thus resulting in the proximal edge of the cupbecoming jagged or sharp and potentially leading to severe skin damagewhen the rodent moves around and the cup's edge rubs against the skin.

In the illustration of FIGS. 45-48, the reinforcement and/or protectivering (170) comprises a proximal flange, an internal conical (funnel) orround section and a distal cylindrical part.

The internal section of the reinforcement and/or protective ring (170)fitting around the animal body may have a conical shape to allow for amore effective fecal entrapment inside the cup in cases where the rodentis allowed to freely mode around, frequently resulting in the tail cupand the animal's tail tilting to the sides away from the longitudinalbody axis. On the other hand, a round shape of the internal section hasthe advantage of serving as a joint surface when the animal moves aroundand the cup rubs against the animal's body/skin. Overall the design ofthe proximal end of the cup should allow for some degree offreedom/motion (not only axial rotation) relatively to the animal'sposterior end, at least partially matching the degree of freedom/motioncharacteristic for the tail base, in order to minimize or eliminate anyinhibition of animal's physical activity/motion.

On the other hand, in the illustration of FIGS. 45-48, the outsidediameter of the flange of the reinforcement and/or protective ring (170)is comparable to or larger than the cross-sectional extension (e.g.diameter) of the cup to ensure that the edge of the proximal surface(end) (160) of the cup (110) is not exposed to chewing. To furtherprevent animals from chewing on the proximal edge of the cup, thereinforcement and/or protective ring (170) may be installed to allow forsome gap (e.g. 2-3 mm) between the flared edge of the ring and the edgeof the proximal surface (edge) of the cup. If desired, the flared edgeof the ring (170) may also be configured to wrap around the edge of thecup. Given that the material of the ring is harder than the material ofthe cup this provides better protection from the animal's teeth.

Additionally, placement of the reinforcement and/or protective ring(170) at the proximal end of the cup (110) may be adjustable in order tocontrol the snug fit of the cup (110) against the animal's posterior endafter installation of the tail sleeve (120).

In its current exemplary implementation, reinforcement and/or protectivering (170) is made by a stainless steel grommet with reduced flange edgediameter and length, performed with a cutter on a lathe to improve sizeand reduce weight, thus resulting in a straight proximal edge of the cup(110).The person skilled in the art will understand that if the proximaledge is carved more anatomically than the reinforcement and/orprotective ring, it will have to be shaped accordingly.

As shown in the illustration of FIGS. 45-48, reinforcement and/orprotective ring (170) is preferably coupled (e.g. attached) to cup (110)using a coupling ring (175) (made of e.g. latex or plastic tubing).Presence of the coupling ring (175) also allows adjusting the depth andplacement of the reinforcement and/or protective ring (170) inside thecup (110), thus tuning the fit or snugness of the cup (110). Embodimentsare also possible where coupling ring (175) is not needed when the cupitself is made such that the reinforcement and/or protective ring (170)can be attached to the cup directly. It should be noted that a simplesoldering and/or gluing of the ring would not be preferred, as it wouldnot allow an adjustable arrangement.

Cup (110) may be made from a clear (e.g. transparent, such aspolypropylene) material to allow for an easier observation of the devicedegree of filling with animal excretions. However, an opaque (e.g.non-transparent) material may be preferred in cases where the excretionsneed to be protected from light, e.g. for further analysis. The materialcan be, for example, a mesh material with an exemplary mesh size of upto 1 mm.

Cup (110) may also comprise venting perforations or boreholes (180) toallow for the fecal excretions to accelerate the desiccation of trappedfecal excretions and prevent moisture entrapment. Advantageously, dryingfecal pellets distribute uniformly within the cup when the animal movesaround. If fecal excretions are not allowed to dry, they couldpotentially stick to the inside surface of the cup and build up aroundthe anus, likely imposing some resistance for further defecation, animportant animal welfare consideration. The venting perforations (180)can be of different shape, size, number, and distribution consideringthat: a) their size should be small enough and their shape (e.g. round)should be designed to prevent the fecal pellets from falling out,especially after drying and/or shrinking. The number of the perforationsshould be sufficiently large and their distribution should besufficiently uniform to allow faster fecal matter desiccation.

Reference will now be made to the tail sleeve (120) of the illustrationof to FIGS. 45-48, which is configured to hold the cup snugly againstthe posterior end of the animal while at the same time maximizingdistributing the opposing force over a larger surface area of the tailskin to reduce the damaging effects of such shear force on the skin andother potential negative effects such as tail strangulation. At the sametime the sleeve (if made from a stiffer material) should only cover afraction of the tail (e.g. less than a half of the total length) toallow for some degree of freedom/motion of the distal part of the tailand not to inhibit the animal's movement.

As shown in FIG. 47, tail sleeve (120) comprises a longitudinally splitor open tubular component (210) (having an exemplary length of 2-5 cm,an exemplary inside diameter of ⅛″-comparable to or slightly smallerthan the tail outside diameter at the tail base- and an exemplaryoutside diameter of ¼″) and an intermediate locking groove (220) on itsoutside surface, the latter configured to allow locking of the tailsleeve (120) through the orifice (140) of the cup (110). In particular,the outside diameter of the groove (220) can be smaller than the outsidediameter of the tubular component (210) and also slightly smaller thanthe orifice or locking opening (140) of the cup (110). In order toaccommodate for variable tail diameters among animals along the taillength, a longitudinal strip of the wall of the sleeve (e.g. 1-2 mmwide) can be optionally removed to prevent uneven tail compression andto facilitate uniform application of adhesive force as later explained.

In the illustration of FIGS. 45-48, the tubular component can becylindrically or conically shaped (to accommodate the slightly conicaltail shape) and may have various degrees of softness or stiffness, butit should be sufficiently soft to conform to the tail shape uponinstallation without applying excessive pressure and sufficiently stiffnot to overstretch or deform in order to withstand the shear force fromthe snugly fit cup (110) when locked onto it. As with the cup (110),also the sleeve (120) may be opaque or clear for easier tail healthmonitoring. In accordance with an embodiment of the disclosure, thetubular component is made from a material devoid of components that uponleaking from the material can be toxic to the animal (e.g.,plasticizer-free tubing) as it can come in contact, in some cases, withthe tail skin through a layer of curable adhesive or adhesive tape, bothof which can potentially aid the extraction of toxic components from thematerial of the tubular component. Potential alternatives can includesurface patterning, e.g. nano- or micro-perforations to providegecko-like adhesion. More generally, any other means that allows theinside surface of the sleeve to be sufficiently adhesive and/or stickycan be devised by the person skilled in the art.

In the illustration of FIGS. 45-48, while the figures show a tail sleevemade of a tubular component cut along its entire length, otherrealizations are possible where the cut partially occurs only for a setlength starting at the proximal end, in order to accommodate the portionof the tail immediately following the tail base, i.e. the part of thetail that is largest in diameter.

The intermediate locking groove (220) extends perpendicularly to thelongitudinal direction of the sleeve (120) along the outside surface ofthe sleeve (120). As already noted above, the groove allows locking ofthe tail sleeve (120) through the orifice (140) of the cup (110). Ifdesired, multiple such locking grooves (220) can be provided in aparallel arrangement along the longitudinal extension of the tail sleeve(120) in order to provide for adjustable locking degrees and extensionsof the sleeve (120) on the cup (110) thus allowing an easy adjustment ofthe snugness of the cup fit once the sleeve is installed on the animal'stail.

Alternatively to the one or more locking grooves (220), the tail sleevecan have a tubular component with a variable outside diameter along itslength, where the proximal (relatively to the desired lockingpoint/level) portion of the tail sleeve has an outside diameter slightlysmaller than the locking opening of the cup, and the distal (relativelyto the desired locking point/level) portion of the sleeve has an outsidediameter larger than the locking opening of the cup.

Additionally, if desired, the distal edge of the tail sleeve may betapered along its inside diameter to prevent distal tail skin (at thedistal edge of the tail sleeve) from bulging up due to the applied shearforce (directed distally) from snug cup fitting and strangulating thedistal end of the tail.

If necessary and/or required, the tail sleeve (120) can be secured tothe tail skin surface of the animal by an adhesive, such as curableadhesive, curable glue, double-sided adhesive tape, the alternativeadhesion means described above. Use of double-sided adhesive tape(opaque or clear for easier tail health monitoring) appears to bepreferable as it allows instantaneous tail sleeve installation. Inparticular, the tape can be pre-applied to the inside surface of thetail sleeve which can be then be almost instantaneously placed onto theanimal's tail while the animal is restrained for a very short amount oftime (about 5-15 seconds). Additionally, with the double-sided adhesivethe tail sleeve and the tail cup can be easily removed and placed backat desired times without causing any skin surface damage.

Advantageously, when mounted, the tail cup (110) may freely rotate alongits longitudinal axis in order to ensure that the edges of the lockingopening (140) do not press too hard on the tail sleeve (120), do notstrangulate the animal's tail and at the same time are not under anexcessive shear force or stress due to the snugness of the cup fit.

Accordingly, in preferred embodiments of methods and systems of thepresent disclosure, preventing coprophagia in the rodent animal isperformed with a tail cup in accordance with any one of the followingenumerated embodiments.

Embodiment 1. A tail cup device (100) for animals with tails isdescribed, the tail cup device (100) comprising:

a tubular-shaped cup (110) for trapping excreted feces of an animal, thetubular-shaped cup having a proximal surface (160) configured to fitaround a posterior end of the animal and a distal surface (130), and

a tail sleeve (120) configured to cover a portion of a tail of theanimal and engageable with the tubular-shaped cup through the distalsurface of the tubular-shaped cup, for mounting of the tubular-shapedcup (110) at a tail base of the animal,

the distal surface of the tubular-shaped cup comprising

an orifice (140) operating as a locking opening of the tubular-shapedcup to allow passing through of the tail sleeve (120) from an inside toan outside of the tubular-shaped cup, the orifice having a diametersmaller than a diameter of the tail sleeve in order to allow a lockingengagement of the tail sleeve with the tubular-shaped cup when appliedto the tail of the animal, and

an unlocking slit (150A-D) for a pressure-based opening of thetubular-shaped cup to engage and disengage the tail sleeve to and fromthe tubular-shaped cup.

Embodiment 2. The device of Embodiment 1, wherein the unlocking slitincludes portions (150A), (150B) across the orifice on the distalsurface, intersecting the orifice and spanning along a diametralextension of the distal surface of the tubular-shaped cup.

Embodiment 3. The device of Embodiment 2, wherein the unlocking slitfurther includes side portions (150C), (150D) extending along oppositeside walls of the tubular-shaped cup.

Embodiment 4. The device of Embodiment 3, wherein the side portionscomprise pressing points (150CC, 150DD) at respective ends of said sideportions, for the pressure-based opening of the tubular-shaped cup.

Embodiment 5. The device of Embodiment 4, wherein the pressing pointshave a circular shape.

Embodiment 6. The device of Embodiment 4, wherein the pressing pointsare located at opposite sides of the tubular-shaped cup.

Embodiment 7. The device of Embodiment 1, wherein the orifice has around or oval shape.

Embodiment 8. The device of Embodiment 1, wherein the orifice iscentrally placed on the distal surface of the tubular-shaped.

Embodiment 9. The device of Embodiment 1, wherein the orifice is placedoff-center on the distal surface of the tubular-shaped cup.

Embodiment 10. The device of Embodiment 9, wherein the unlocking slitcrosses the orifice along a chord extension of the distal surface of thetubular-shaped cup.

Embodiment 11. The device of Embodiment 1, wherein a cross section ofthe tubular-shaped cup is selected from a group consisting of circularcross section, oval cross section and rectangular cross section.

Embodiment 12. The device of Embodiment 1, wherein the tubular-shapedcup has a uniform cross section along a length of the tubular-shapedcup.

Embodiment 13. The device of Embodiment 1, wherein the tubular-shapedcup has a non-uniform cross section along a length of the tubular-shapedcup, a larger cross section being provided close to one end of thetubular-shaped cup and a smaller cross section being provided close toanother end of the tubular-shaped cup.

Embodiment 14. The device of Embodiment 1, wherein the tubular-shapedcup has a length of 2-5 cm and a diameter of 1-3 cm.

Embodiment 15. The device of Embodiment 1, further comprising areinforcement and/or protective ring (170) located along the proximalsurface of the tubular-shaped cup.

Embodiment 16. The device of Embodiment 15, wherein the reinforcementand/or protective ring comprises an anatomically carved proximal flange,an internal conical or round section and a distal cylindrical part.

Embodiment 17. The device of Embodiment 15, wherein placement of thereinforcement and/or protective ring along the proximal surface of thetubular-shaped cup is adjustable.

Embodiment 18. The device of Embodiment 15, wherein the reinforcementand/or protective ring is controllably coupled to the tubular-shaped cupthrough a coupling ring (175).

Embodiment 19. The device of Embodiment 1, wherein the tubular-shapedcup is made of a transparent material.

Embodiment 20. The device of Embodiment 1, wherein the tubular-shapedcup comprises venting perforations (180).

Embodiment 21. The device of Embodiment 1, wherein the tail sleeve isrotatable along its longitudinal axis.

Embodiment 22. The device of Embodiment 1, wherein the tail sleevecomprises a first portion inside the tubular-shaped cup and a secondportion outside the tubular-shaped cup.

Embodiment 23. The device of Embodiment 22, wherein the tail sleevefurther comprises at least one locking groove between the first portionand the second portion, the at least one locking groove configured toallow locking of the tail sleeve through the orifice of thetubular-shaped cup.

Embodiment 24. The device of Embodiment 23, wherein the at least onelocking groove are a plurality of locking grooves to allow an adjustableengagement of the tail sleeve with the tubular-shaped cup.

Embodiment 25. The device of Embodiment 22, wherein the second portionof the tail sleeve comprises a distal edge tapered along an insidediameter of the second portion.

Embodiment 26. The device of Embodiment 22, wherein the tail sleevecomprises a tubular component with a variable outside diameter along itslength, wherein a proximal portion of the tail sleeve has an outsidediameter smaller than a diameter of the orifice, and a distal portion ofthe tail sleeve has an outside diameter larger than the diameter of theorifice.

Additional embodiments, of the preferred tail cup device according tothe disclosure are identifiable by a skilled person.

In several embodiments, the modifying as described herein is typicallyfollowed by the quantifying absolute abundance of the target prokaryoteson a rodent modified sample from the modified rodent to provide adetected rodent modified presence, rodent modified proportion and/orrodent modified total load of target prokaryotes to be compared with thetarget presence, proportion and/or total load of target prokaryotes,according to the method of the disclosure.

In some embodiments, the quantifying absolute abundance of the targetprokaryotes, can further be performed on a rodent sample before themodifying to obtain a detected rodent presence, rodent proportion and/orrodent total load of target prokaryotes to be compared with the targetpresence, proportion and/or total load of target prokaryotes and/or topossibly be used as a reference presence, proportion and/or referencetotal load of target prokaryotes to guide selection of type and amountof compounds agents and/or prokaryotes to be introduced enriched and/ordepleted in the modifying according to the methods and systems of thedisclosure.

Described herein are also customized rodent models having a target gutmicrobiome profile formed by a target presence, a target proportionand/or a target total load of a target prokaryote of a target taxon,obtained by methods herein described to provide a customized rodentmodel.

In preferred embodiments, customized rodent models are customized tocomprise target individual microbiome or target averaged microbiome aswill be understood by a skilled person. In some of those embodiments,the target individual or average microbiome mimicks the microbiome ofthe individual or plurality of the individuals under selectedphysiological and/or pathological conditions, such as any one of theconditions herein described and additional conditions identifiable by askilled person.

In some embodiments, customized rodents according to the presentdisclosure comprise rodent model modified before or after quantifyingabsolute abundance of target prokaryotes according to method of thepresent disclosure.

In some exemplary embodiments, customized rodent model of the presentdisclosure comprise germ-free rodents (understood as a rodents devoid ofmicrobial flora, including eukaryotes, prokaryotes, and viruses)[www.taconic.com/prepare-your-model/microbiome-solutions-and-germ-free-mice/germ-free-mice/];Rodents pre-treated with antimicrobial agents (e.g., antibiotics andantibiotic cocktails) to deplete or even eliminate their nativemicrobiota; Gnotobiotic rodents which are rodents carrying a definedmicrobiome, consisting of multiple or individual prokaryotes modifiedthrough microbiome and/or genomic modification procedures, before orafter quantifying absolute abundance of target prokaryotes according tomethod of the present disclosure.

In some exemplary embodiments, customized rodent model of the presentdisclosure further comprise humanised rodents—(chimeric) rodentscarrying human donor (or specific-patient)-derived cells in theirtissues/organs obtained via transplantation of human peripheral blood,bone marrow, lymphoid, liver [102, 103][www.herabiolabs.com/humanized-liver-mice/,www.yecuris.com/frg-ko-mice/], spleen cells and cells from othertissues, including donor/patient skin.

Patient-Derived Xenograft (PDX) model can be developed by means ofinoculating the rodent with patient-derived tumor cells/tissues forpre-clinical oncology research, including the discovery of tumorpresence biomarkers, biomarkers of the tumor susceptibility to drugs,and screening of anticancer therapeutics[www.criver.com/products-services/discovery-services/pharmacology-studies/oncology-immuno-oncology-studies/oncology-study-models/patient-derived-xenografts-pdx-models?region=3601],modified through microbiome and/or genomic modification procedures,before or after quantifying absolute abundance of target prokaryotesaccording to method of the present disclosure.

In some preferred embodiments, customized rodent model obtained by themethod of the disclosure comprises a customized microbiome of the GIT ofan individual, preferably a human being.

In some preferred embodiments, customized rodent model obtained by themethod of the disclosure comprises a customized rodent model wherein therodent model has been modified by preventing coprophagia to provide therodent with a “human-like pattern” of microbial colonization such as therodents obtained by preventing coprophagia with preferred tail cup ofthe disclosure.

Customized rodent with a “human-like pattern” of microbial colonizationmodel have a microbiota compartmentalization in the GIT mimicking themicrobial distribution of human beings. Such customized model is rodentmodel with compartmentalized gastrointestinal microbiome” which can beused in testing which affect the microbial function and phenotype(including metabolism and response to xenobiotics).

Customized rodent models can be used in methods and systems to performtesting of effects of a compound on physiological and/or pathologicalconditions associated with a target microbiome, having a targetmicrobiome profile, the method comprises providing a customized rodentmodel according to the present disclosure having the target microbiomeprofile, and performing the testing on the customized rodent model.

Exemplary testing comprises testing of impact of diets, test ofmolecules, and in particular, diet, testing of sleep cycle andadditional testing identifiable by a skilled person.

Methods and systems herein described can also be used to identify targetprokaryotes whose presence of one or more physiological or pathologicalcondition. In some of those embodiments, if certain prokaryotes in themicrobiome of an individual having a disease have a relative or anabsolute abundance that is substantially different from that of theprokaryotes in a healthy individual, these prokaryotes can be identifiedas signature prokaryotes of that disease. For example, reference is madethe procedure reported in Examples 20-26 wherein detection ofprokaryotes performed in patients with and without Crohn's disease tofind that Enterobacteriaceae, Pasteurellaceae, Fusobacteriaceae, andNeisseriaceae are enriched in Crohn's disease patients and can be usedas target prokaryotes whose presence, proportion and total load can be asignature of a target microbiome associated with Crohn's disease.

All methods of the present disclosure can be performed with acorresponding system comprising a rodent, a customized rodent, and/orprimers specific for the 16S rRNA conserved regions specific for theplurality of sample 16S rRNAs, reagents to perform polymerase chainreaction, and/or reagents to perform amplicon sequencing, and/or testingof a compound or performing other testings, for simultaneous combined orsequential use to detect an absolute abundance of the target 16S rRNAsin the sample according to the method herein described.

In some embodiments, the system comprises primers specific for 16S rRNAconserved regions specific for the sample taxon, reagents to performpolymerase chain reaction, and reagents to perform amplicon sequencingfor simultaneous combined or sequential use to detect an absoluteabundance of the target taxon in the sample according to the methodherein described.

The primers used herein comprise the target primer sequence specific for16S rRNA conserved regions alone or in combination with adapter,barcode, tag, linker, pad and/or frameshifting sequence describedherein.

In some embodiments, the systems further comprise buffers, enzymeshaving polymerase activity, enzymes having polymerase activity andlacking 5′-3′ exonuclease activity or both 5′ to 3′ and 3′ to 5′exonuclease activity, enzyme cofactors such as magnesium or manganese,salts, chain extension nucleotides such as deoxynucleoside triphosphates(dNTPs), modified dNTPs, nuclease-resistant dNTPs or labeled dNTPs,necessary to carry out an assay or reaction, such as amplificationand/or detection of alterations in target nucleic acid sequencescorresponding to the specific 16S rRNA described herein.

In some embodiments, the systems of the disclosure to be used inconnection with methods herein described, the reagents comprise DNAextraction, RNA extraction kit and amplification mix. The system canalso include reagents required for preparing the sample, such as one ormore of buffers e.g. lysis, stabilization, binding, elution buffers forsample preparation, enzyme for removal of DNA e.g. DNase I, and solidphase extraction material for sample preparation, reagents required forquantitative detection such as intercalating dye, reverse-transcriptionenzyme, polymerase enzyme, nuclease enzyme (e.g. restriction enzymes;CRISPR-associated protein-9 nuclease; CRISPR-associated nucleases asdescribed herein) and reaction buffer. Sample preparation materials andreagents may include reagents for preparation of RNA and DNA fromsamples, including commercially available reagents for example from ZymoResearch, Qiagen or other sample preparations identifiable by a skilledperson. The system can also include means for performing DNA or RNAquantification such as one or more of: container to define reactionvolume, droplet generator for digital quantification, chip for digitaldetection, chip or device for multiplexed nucleic acid quantification orsemi-quantification, and optionally equipment for temperature controland detection, including optical detection, fluorescent detection,electrochemical detection.

In some embodiments where the system can be used to perform a singlestep quantification (BC-qPCR) according to the disclosure the system cancomprise a “standard” (anchor)—sample containing either single orcomplex microbial 16S DNA of known concentration (copy number), such asthe one (ZymoBIOMICS Microbial Community DNA Standard, Zymo Research,Irvine, Calif., USA) described in [104] and additional standardsidentifiable by a skilled person. In some exemplary embodiments, thestandard can consists of 10 microorganisms, 8 of which are bacteria(Listeria monocytogenes, 12%; Pseudomonas aeruginosa, 12%; Bacillussubtilis, 12%; Escherichia coli, 12%; Salmonella enterica, 12%;Lactobacillus fermentum, 12%; Enterococcus faecalis, 12%; Staphylococcusaureus, 12%) with 16S genes. These taxa are mixed together at definedconcentrations so that the expected outcome of extraction and sequencingis known. The absolute concentration of 16S copies in such standard canbe either estimated from the total DNA concentration (e.g., 10ng/microL) and the approximate genome size of the members of thisdefined community. Alternatively, the absolute concentration of 16Scopies in such standard can be directly measures by digital PCR as willbe understood by a skilled person. Additional exemplary standardcomprise samples of nucleic acids extracted from other complex mixturesof microorganisms (e.g., stool) or from pure microbial cultures (e.g.,E. coli) can be quantified using digital PCR and serve as absolutequantification standards for qPCR and BC-qPCR assays described herein.

The systems herein describe can also include other necessary reagents toperform any of the NGS techniques disclosed herein. For example, thesystems can further comprise one or more of: adapter sequences, barcodesequences, reaction tubes, ligases, ligase buffers, wash buffers and/orreagents, hybridization buffers and/or reagents, labeling buffers and/orreagents, and detection means. The buffers and/or reagents are usuallyoptimized for the particular amplification/detection technique for whichthe system is intended. Protocols for using these buffers and reagentsfor performing different steps of the procedure can also be included inthe system.

In some embodiments, the system can comprise a device combining allaspects required for the absolute quantification of the 16S rRNA hereindescribed.

The systems herein disclosed can be provided in the form of kits ofparts. In kit of parts for performing any one of the methods hereindescribed, the primers and the reagents for the related detection andquantification can be included in the kit. The kit can further containoligonucleotide (oligo) sequences of barcodes, adapters, linkers, padand/or frameshifting bases compatible for next-generation sequencingplatforms.

In a kit of parts, the primers and the reagents for the relateddetection, quantification and sequencing, and additional reagentsidentifiable by a skilled person are comprised in the kit independentlypossibly included in a composition together with suitable vehiclecarrier or auxiliary agents. For example, one or more probes can beincluded in one or more compositions together with reagents fordetection also in one or more suitable compositions.

Additional components can include labeled polynucleotides, labeledprimer such as barcoded with an adapter sequence for next generationsequencing, labels, microfluidic chip, reference standards, andadditional components identifiable by a skilled person upon reading ofthe present disclosure.

The terms “label” and “labeled molecule” as used herein refer to amolecule capable of detection, including but not limited to radioactiveisotopes, fluorophores, chemiluminescent dyes, chromophores, enzymes,enzymes substrates, enzyme cofactors, enzyme inhibitors, dyes, metalions, nanoparticles, metal sols, ligands (such as biotin, avidin,streptavidin or haptens) and the like. The term “fluorophore” refers toa substance or a portion thereof which is capable of exhibitingfluorescence in a detectable image. As a consequence, the wording“labeling signal” as used herein indicates the signal emitted from thelabel that allows detection of the label, including but not limited toradioactivity, fluorescence, chemoluminescence, production of a compoundin outcome of an enzymatic reaction and the like.

In embodiments herein described, the components of the kit can beprovided, with suitable instructions and other necessary reagents, inorder to perform the methods here disclosed. The kit will normallycontain the compositions in separate containers. Instructions, forexample written or audio instructions, on paper or electronic supportsuch as tapes, CD-ROMs, flash drives, or by indication of a UniformResource Locator (URL), which contains a pdf copy of the instructionsfor carrying out the assay, will usually be included in the kit. The kitcan also contain, depending on the particular method used, otherpackaged reagents and materials (i.e. wash buffers and the like).

Further details concerning the identification of the suitable carrieragent or auxiliary agent of the compositions, and generallymanufacturing and packaging of the kit, can be identified by the personskilled in the art upon reading of the present disclosure.

EXAMPLES

The methods of the disclosure and related compositions, and systemsherein described are further illustrated in the following examples,which are provided by way of illustration and are not intended to belimiting.

In particular, the following examples illustrate exemplary methods andprotocols for performing methods directed to detect absolutequantification of nucleic acids and particularly 16S rRNA nucleic acids.

In particular, Examples 1-4 below describe general protocols andexperimental procedures conducted for quantitative microbiome profilingin luminal and tissue samples with broad coverage and dynamic rangeusing simultaneous real-time PCR quantification of 16S rRNA gene DNAcopy and amplicon barcoding for multiplexed next-generation sequencingfrom the same analyzed sample performed in a combined workflow.

Examples 5-10 below describe general protocols and experimentalprocedures conducted for detecting absolute abundance measurements ofmucosal and luminal microbial communities using the methods and systemsherein described. In particular, these examples describe a quantitativeframework to measure absolute abundances of individual bacterial taxa bycombining the digital PCR with the high-throughput 16S rRNA geneamplicon sequencing. In a murine ketogenic-diet study, microbial loadsin lumenal and mucosal samples along the GI tract were compared.Quantitative measurements of absolute abundances reveal decreases intotal microbial loads on the ketogenic diet and enable one to determinethe differential effects of diet on each taxon in stool andsmall-intestine mucosa samples. This quantitative microbial analysisframework, suitable for diverse GI locations, enables mapping microbialbiogeography of the mammalian GI tract and more accurate analyses ofchanges in microbial taxa in microbiome studies.

Examples 11-17 below describe general protocols and experimentalprocedures conducted for self-reinoculation with fecal flora in miceusing the methods and systems herein described. In particular, theseexamples used quantitative 16S rRNA gene amplicon sequencing,quantitative microbial functional gene content inference, andmetabolomic analyses of bile acids to evaluate the effects ofself-reinoculation on microbial loads, composition, and function in themurine upper gastrointestinal tract.

Examples 18-19; below describe a tail-cup device of the disclosureproviding a preferred mean to prevent coprophagia in rodents accordingto the present disclosure and general description of a Rodent Model witha “humanized” digestive tract obtained by the combined used of absolutequantification method and the tail cup device of the disclosure.

Examples 20-25 below describe an exemplary detection of absolutemicrobial loads in the human duodenum and their potential relationshipwith factors related to health and disease in 250 individuals from theREIMAGINE study[105], as well in the oral microbiome in a subset of 21individuals from this cohort. The total load of the e human duodenal andoral microbiome can be used to provide customized rodent model withtarget duodenal and oral microbiome that can be used in testing of themakeup of the human duodenal microbiome, improve the understanding ofthe underlying community structure of SIBO, and determine how microbialload and composition correlate with upper GI symptoms.

A person skilled in the art will appreciate the applicability and thenecessary modifications to adapt the features described in detail in thepresent section, to additional methods and related compositions andsystems according to embodiments of the present disclosure.

Example 1: General Protocols for Performing 16S rRNA Gene DNAQuantification and Amplicon Barcoding Workflow for QuantitativeMicrobiome Profiling

An exemplary quantitative microbiome profiling was performed accordingto the general protocol schematically illustrated in FIG. 1.

Samples were collected from mice and the related DNA extracted (FIG. 1Panel A).

In particular, all animal handling and procedures were performed inaccordance with the California Institute of Technology (Caltech)Institutional Animal Care and Use Committee (IACUC).

Fecal samples were collected from SPF C57BL6/J mice of 2-12 months ofage originally purchased from Jackson Laboratory (Sacramento, Calif.,USA) and housed in the Caltech animal facility for up to 10 months. Germfree mouse intestinal mucosal samples from germ free C57BL6/J mice of2-5 months of age obtained from the germ-free mouse colony maintained inthe Caltech animal facility were collected and processed as in [1, 2].

The total DNA was extracted from fecal and mucosal samples preserved inDNA/RNA Shield (DRS) solution (R1100-250, Zymo Research, Irvine, Calif.,USA) or fresh using ZymoBIOMICS DNA Miniprep Kit (D4300, Zymo Research)as described in [1, 2].

Quantitative (linear) recovery of DNA in the range of 16S rRNA genecopies of 10³-10 per ml was verified in using a series of 10-folddilutions of specific-pathogen-free (SPF) mouse fecal microbialsuspension in saline.

The absolute abundance of the microorganisms of the microbiome wasperformed by BC-qPCR reactions prepared in replicates for more accuratequantification and uniform amplicon barcoding. (FIG. 1 Panel B).

In particular, Quantitative PCR (qPCR) for 16S rRNA gene DNA copyenumeration was performed according to the method described in detail in[1, 2] which are incorporated by reference in their entirety.

Briefly, each qPCR reaction was set up with 1.5 μL of DNA sample, qPCRmaster mix (SsoFast EvaGreen Supermix, #172-5200, Bio-Rad Laboratories,Hercules, Calif., USA), forward (UN00F2, 5′-CAGCMGCCGCGGTAA-3′) (SEQ IDNO: 25) and reverse (UN00R0, 5′-GGACTACHVGGGTWTCTAAT-3′ [16, 18])primers (SEQ ID NO: 26) (Integrated DNA Technologies, San Diego, Calif.,USA) at the final concentration of 500 nM each, and ultrapure water(Invitrogen UltraPure DNase/RNase-Free Distilled Water 10977-015, ThermoFisher Scientific) to the final volume of 15 μL.

The thermocycling program was set up as follows: initial denaturation at95° C. for 5 min. followed by 40 cycles each consisting of denaturationat 95° C. for 15 sec., annealing at 53° C. for 10 sec., and extension at68° C. for 45 sec.

Assay was performed on a real-time PCR instrument (CFX96 Real-Time PCRDetection System, Bio-Rad Laboratories). The raw fluorescence data wereprocessed and the Cq values were extracted with the accompanyingsoftware (Bio-Rad CFX Manager 3.1, #1845000, Bio-Rad Laboratories).

Amplification and barcoding were performed under real-time fluorescencemeasurements on a real-time PCR instrument (FIG. 1 Panel C).

In particular, 16S rRNA gene DNA amplicon barcoding for next generationsequencing (NGS) was performed with a method is described in detail in[1, 2]. Briefly, all DNA samples were amplified and barcoded intriplicates for. Each reaction was set up with 3 μL of DNA samplecombined with the PCR master mix (5PRIME HotMasterMix, #2200400,Quantabio, Beverly, Mass., USA), ×1 DNA intercalating dye (EvaGreen,#31000, Biotium, Fremont, Calif., USA), barcoded forward (UN00F2_BC,5′-AATGATACGGCGACCACCGAGATCTACACTATGGTAATTGTCAGCMGCCGCGGTAA-3′) (SEQ IDNO: 75) and reverse (UN00R0,5′-CAAGCAGAAGACGGCATACGAGAT[NNNNNNNNNNNN]AGTCAGTCAGCCGGACTACHVGGGTWTCTAAT-3′ [16, 18], (SEQ ID NO: 76) where [NNNNNNNNNNNN]—12-nucleotide barcode sequences from [16]) primers (Integrated DNATechnologies) at the final concentration of 500 nM each, and ultrapurewater (Thermo Fisher Scientific) to the final volume of 30 μL.

The thermocycling program was set up similarly to the EMP protocol [16,18] as follows: initial denaturation at 94° C. for 3 min. followed byvariable for each sample number of cycles each consisting ofdenaturation at 94° C. for 45 sec., annealing at 54° C. for 60 sec.,extension at 72° C. for 105 sec.; followed by a final extension step at72° C. for 10 min.

Assay was performed on a real-time instrument (CFX96 Real-Time PCRDetection System, Bio-Rad Laboratories). Samples were amplified for avariable number of cycles and each sample was removed from the heatingblock during the last 15 sec. of the current cycle extension step uponreaching the mid-exponential amplification phase. Each removed samplewas maintained on a secondary heating block at 72° C. until all samplesfrom the amplification series were amplified and returned to the primaryheating block for the final extension step. The raw fluorescence datawere processed and the fluorescence profiles over time were extractedwith the accompanying software (Bio-Rad CFX Manager 3.1, #1845000,Bio-Rad Laboratories).

Endpoint amplification products from whole PCR reactions were diluted4-fold in ultrapure water (Invitrogen) and analyzed by gelelectrophoresis using 1% (E-Gel EX, #G401001, Thermo Fisher Scientific)and 2% agarose gels (E-Gel, #G501802, Thermo Fisher Scientific).

Barcoding PCR real-time data processing was performed by processing rawfluorescence data using Python tools (Python tools used are describedand referenced in [1, 2]).

Amplification profiles (fluorescence) for each PCR sample replicate werebaseline-corrected by subtracting the minimal fluorescence value fromthe first 15 amplification cycles for each amplification replicate.Baseline-corrected amplification profiles from all replicates wereaveraged for each sample. Baseline-corrected and averaged amplificationprofiles were then used to find the Cq values (cycle numbers) at whichthey reached the fluorescence threshold (chosen as 2000 RFU) byinterpolation.

The quantitative PCR data (Cq values) so obtained were recorded (FIG. 1Panel D).

Cq values were converted to absolute fold-difference values in total 16SrRNA gene copy load using the equations 3.1 and 3.2 (FIG. 2) andassuming the BC-qPCR efficiency of 95.0%. The absolute fold differencevalues were then used to convert the taxa 16S rRNA gene relativeabundance data obtained from the next generation sequencing to the taxa16s rRNA gene absolute fold-difference data.

Barcoded samples were then quantified, pooled, purified, and sequencedon an NGS instrument (FIG. 1 Panel E).

In particular, Digital PCR (dPCR) for Illumina library quantification:was performed as described in detail in [1, 2]. Briefly, a home-brewdigital PCR library quantification assay was set up using the IlluminaP5 and P7 adapter sequences as priming sites [16, 18-21].

Each reaction was set up with 2.0 μL of the diluted amplicon sampleligated with the Illumina adapters, ddPCR master mix (QX200 ddPCREvaGreen Supermix, #186-4033, Bio-Rad Laboratories), forward(ILM00F(P5), 5′-AATGATACGGCGACCACCGA-3′ (SEQ ID NO: 77),) and reverse(ILM00R(P7), 5′-CAAGCAGAAGACGGCATACGA-3′ (SEQ ID NO: 78)) primers(Integrated DNA Technologies) at the final concentration of 125 nM each,and ultrapure water (Invitrogen) to the final volume of 20 μL.

Thermocycling program was set up as follows: initial denaturation at 95°C. for 5 min, followed by 40 cycles each consisting of denaturation at95° C. for 30 sec. and annealing-extension at 60° C. for 90 sec.;followed by the dye stabilization step consisting of 5 min incubation at4° C., 5 min incubation at 90° C., and incubation at 12° C. for at least5 min.

This step was performed to sequence multiple samples barcoded withdifferent unique barcodes to quantify the concentration of each barcodedsample 9 (e.g. using PCR assay or with other alternative non-PCRmethods). and to quantify the amplicon concentration in the library notto overload/underload the sequencing flow cell as will be understood bya skilled person.

Hardware setup and droplet analysis were performed as in “Digital PCR(dPCR) for absolute 16S rRNA gene DNA copy enumeration”.

Library pooling, purification, and quality control were performedaccording to the method described in detail in [1, 2]. Briefly,triplicates of each barcoded amplicon sample were combined. After eachsample was quantified with the home-brew ddPCR library quantificationassay and KAPA SYBR FAST Universal qPCR Library Quantification Kit(#KK4824, Kapa Biosystems, Wilmington, Mass., USA), all samples werepooled in equimolar quantities. The library was purified with AgencourtAMPure XP beads (#A63880, Beckman Coulter, Brea, Calif., USA) and elutedwith ultrapure water (Invitrogen). Quality control on the pooled librarywas performed using light absorbance at 260/280 nm (NanoDrop 2000c,Thermo Fisher Scientific) and the mean amplicon size of ˜400 nucleotideswas confirmed with a High Sensitivity D1000 ScreenTape System(#5067-5584 and #5067-5585, Agilent Technologies, Santa Clara, Calif.,USA) on a 2200 TapeStation instrument (Agilent Technologies) supportedwith the Agilent 2200 TapeStation Software A02.01. (AgilentTechnologies).

Next generation sequencing was then performed according to the: s methoddescribed in detail in [1, 2]. Briefly, paired-end 300-base reads weregenerated on a MiSeq instrument (Illumina, San Diego, Calif., USA) usinga MiSeq Reagent Kit v3 (#MS-102-3003, Illumina) with a PhiX controlspiked in at 15%.

The following sequencing primers were used:

MiSeq read 1: Seq_UN00F2_Read_1, (SEQ ID NO: 79)5'-TATGGTAATTGTCAGCMGCCGCGGTAA-3'. MiSeq read 2: Seq_UN00F2_Read_1,(SEQ ID NO: 80) 5'-AGTCAGTCAGCCGGACTACHVGGGTWTCTAAT-3' MiSeq index read: Seq_UN00R0_RC_Index, (SEQ ID NO: 81)5'-ATTAGAWACCCBDGTAGTCCGGCTGACTGACT-3' 

NGS sequencing results provide data on relative abundances of microbialtaxa (FIG. 1 Panel F).

With respect to the sequencing read processing and sequencing dataprocessing, all processed and analyzed NGS data were obtained from [1,2].

NGS data and sequencing data analysis scripts were obtained and areavailable from [1, 2].

Microbiota relative abundance profiles were converted to microbiotaabsolute or absolute fold-difference abundance profiles using theabsolute or absolute fold-difference data on 16S rRNA gene DNA loads inthe corresponding samples measured in the step schematically illustratedin (FIG. 1 Panel D). (FIG. 1 Panel G).

The validated the accuracy of the quantitative 16S rRNA gene ampliconprofiling obtained using the single-step BC-qPCR approach was confirmedcomparing the related result with the quantitative 16S rRNA geneamplicon profiling results obtained using real-time and digital PCR100421 In particular, the Digital PCR (dPCR) for absolute 16S rRNA geneDNA copy enumeration was performed according to the: method is describedin detail in [1, 2]. Briefly, each reaction was set up with 2.0 μL ofDNA sample, ddPCR master mix (QX200 ddPCR EvaGreen Supermix, #1864033,Bio-Rad Laboratories), forward (UN00F2, 5′-CAGCMGCCGCGGTAA-3′) (SEQ IDNO: 25) and reverse (UN00R0, 5′-GGACTACHVGGGTWTCTAAT-3′ [16, 18])primers (SEQ ID NO: 26) (Integrated DNA Technologies) at the finalconcentration of 500 nM each, and ultrapure water (Thermo FisherScientific) to the final volume of 20 μL.

In some experiments, additional DNA intercalating dye (EvaGreen, #31000,Biotium, Fremont, Calif., USA) was added to the reactions up to ×1 finalconcentration (to achieve up to ×2 overall concentration).

Each reaction volume was converted to droplets using a QX200 dropletgenerator (#1864002, Bio-Rad Laboratories).

Droplet samples were amplified on a thermocycler (C1000 Touch, #1841100,Bio-Rad Laboratories) according to the program: initial denaturation at95° C. for 5 min. followed by 40 cycles each consisting of denaturationat 95° C. for 30 sec., annealing at 52° C. for 30 sec., and extension at68° C. for 60 sec.; followed by the dye stabilization step consisting of5 min incubation at 4° C., 5 min incubation at 90° C., and incubation at12° C. for at least 5 min.

Droplet samples were quantified on a QX200 Droplet Digital PCR System(#1864001, Bio-Rad Laboratories) The raw data were analyzed and thetarget molecule concentrations were extracted using the accompanyingsoftware (QuantaSoft Software, #1864011, Bio-Rad Laboratories).

Example 2: Design of Primers Specific for Low Copy Number MicrobialTemplates in Samples with High Content of Mouse or Human Host DNABackground

Primers from the Earth Microbiome Project (EMP) 16S rRNA gene ampliconprofiling protocol [16, 18] were optimized for use in the absolutequantification according to the method described in Example 1 to improvebroad-coverage 16S rRNA gene DNA quantification via real-time anddigital PCR in the presence of high host DNA background.

Accordingly the EMP 16S rRNA gene amplicon profiling protocol [16,18] iswell-known for having broad microbial coverage and has been widelyadopted in the field of basic and clinical microbiome research.

It was hypothesized that by redesigning the EMP forward primer(designated by Applicant as UN00F0) at its 5′ end to start at position519 (UN00F2) of the V4 region of microbial 16S rRNA gene sequence (FIG.3, panel A) its nonspecific annealing to the mouse and humanmitochondrial 12S rRNA gene DNA—the main competing template of mammalianorigin identified by amplicon sequencing of PCR products obtained withmouse germ-free tissue DNA—would be either reduced or eliminated. Suchchange would increase the primer's specificity for low copy numbermicrobial templates in samples with high content of mouse or human hostDNA background.

The effectiveness of these design considerations was confirmed byperforming qPCR reactions in complex mouse microbiota DNA samplesanalyzed as-obtained or spiked with GF mouse small-intestine mucosal DNAat 100 ng/μL. The ˜200-bp mitochondrial 12S rRNA gene amplicons wereabsent in the PCR reactions containing high amounts of mouse DNA andusing the modified forward primer UN00F2 (FIG. 3, panel B).

The efficiency of the quantitative PCR reactions set up with themodified forward primer UN00F2 was similar (and high) with and withoutthe presence of 100 ng/μL of mouse DNA in the template sample (FIG. 3,panel C) demonstrating the robust assay performance.

The qPCR experiments also suggested that the PCR reactions with highhost DNA background are intercalating dye-limited: the increase in totalfluorescence (A-RFU) in each reaction at the end of amplification waslower in samples containing 100 ng/μL of background mouse DNA whereasthe total fluorescence levels were similar between samples with andwithout the background mouse DNA.

By combining the use of the new forward primer UN00F2 with thesupplementation of commercial reaction mix with additional amounts ofintercalating EvaGreen dye improved the digital PCR performance byincreasing the separation between negative and positive droplets in thedroplet digital PCR (ddPCR) reactions used for quantifying 16S rRNA geneDNA copies in samples with high host DNA background (100 ng/μL) (FIG. 3,panel D). This assay was used to establish or confirm the exact 16S rRNAgene DNA copy numbers in the standard samples, which were furtherutilized to build the standard curves in the qPCR assays.

Additionally, the modification of the primer set UN00F2+UN00R0 broadenedits taxonomical coverage of the microbial diversity (86.0% Archaea,87.0% Bacteria) compared with the original EMP primer set UN00F0+UN00R0(52.0% Archaea, 87.0% Bacteria) based on the SILVA 16S rRNA genesequence reference database [9, 55, 56].

The above results therefore demonstrate that optimized primers improvebroad-coverage 16S rRNA gene DNA quantification via real-time anddigital PCR in the presence of high host DNA background.

Example 3: Features of Primers Specific for Low Copy Number Microbial16S rRNA Gene in the Amplification and Amplicon Barcoding of the 16SrRNA Recognition Segment in High Host DNA Background

The modified barcoded primers used in the optimized workflow accordingto Example 2 were tested for the features enabling simultaneous 16S rRNAgene DNA copy quantification and amplicon barcoding in samples with highhost DNA background

Two essential and contrasting design principles in the BC-qPCR reactionoptimization guide this work:

1. The amplification and barcoding reaction utilizing degenerate 16SrRNA gene primers (whether using the original EMP primers or improvedEMP primers) should be conducted at the lowest possible annealingtemperature (within the range of annealing temperatures for the specificprimer variants within the degenerate primer mixture) to maximize theuniformity of amplification of diverse 16S rRNA gene DNA sequences andeliminate the amplification biases.

2. The amplification and barcoding reaction should be conducted at thehighest possible annealing temperature to minimize the primer dimerformation and non-specific host mitochondrial DNA amplification both ofwhich would be competing with specific microbial 16S rRNA gene DNAtemplate for reaction resources (dNTPs, primers, polymerase,intercalating dye). Such competing reactions would inevitably havepronounced effects on the samples containing very low levels of specificmicrobial template and requiring high numbers of amplification cycles.

Compared with the improved primer set (UN00F2+UN00R0), the original EMPprimer set (UN00F0+UN00R0) requires a higher annealing temperature toreduce primer dimer formation and amplification of mouse mitochondrial(MT) DNA.

Long “overhangs” (carrying the linker and Illumina adapter sequences) atthe 5′ end of the forward primer and non-complimentary to the specific16S rRNA gene DNA template were not sufficient to prevent the EMP primerset from amplifying the mouse MT DNA. At 53.9° C. both primer dimers andMT DNA amplification persisted in the reactions using the EMP primers,which suggested that this primer set would require even higher annealingtemperatures (>53.9° C.) to eliminate the amplification artifacts.

This in turn will likely introduce amplification biases across a rangeof specific 16S rRNA gene DNA templates. Using the improved primer seteliminated both artifacts in the reactions conducted at 53.9° C. (FIG.4, panel A), while some primer dimer formation was still present in thereactions conducted at 52° C. Thus, the temperature of 54° C. wasselected as optimal for the BC-qPCR reaction.

The BC-qPCR assay demonstrated good performance in samples with andwithout high host DNA background (GF mouse DNA spiked in at 100 ng/μL ofthe DNA template sample) and containing the specific complex microbiotatemplate (SPF mouse fecal DNA) across multiple orders of concentration(FIG. 4, panel B). Regardless of the presence of high host DNAbackground, the reaction efficiency was ˜95.0% and the assay was able toresolve 1.25 to 1.67-fold differences in total 16S rRNA gene copy loadsamong samples within the range of 10⁴-83-1010-95 copies/mL.

The data also confirmed the BC-qPCR assay can provide accuratequantification data for the amount of 16S rRNA gene DNA copy loads inthe analyzed samples. The Cq values obtained based on the real-timefluorescence measurements during the BC-qPCR reaction were in a goodagreement with the absolute 16S rRNA gene DNA copy values (FIG. 4, panelC) estimated in the same samples (samples and data were from [1, 2])using the previously optimized qPCR assay (FIG. 3, panel C).

This example demonstrates features of specific barcoded primers odExample 2 enable simultaneous 16S rRNA gene DNA copy quantification andamplicon barcoding in samples with high host DNA background according tomethods of the disclosure according to Example 1.

Example 4: Absolute Quantification of Microbiota and MicrobiotaQuantitative Microbiota Profiling by Detection of Absolute FoldDifferences

Absolute quantification of microbiota according to the method of Example1 was performed by detecting absolute fold differences between differentsamples.

In particular, a single-step BC-qPCR approach was used to calculate theabsolute fold differences (as in FIG. 2, panel C) for a number of taxa(FIG. 5) among samples from four experimental groups of mice describedin [1, 2] using the NGS data from [1, 2] and the absolute folddifference data for the total 16S rRNA gene abundance in thecorresponding samples from BC-qPCR assay (see Example 1).

The absolute fold difference data for each individual taxon that areyielded by the single step approach can be used for comparisons amonggroups subjected to different experimental conditions usingnon-parametric rank tests (e.g., Kruskal-Wallis).

Such comparisons revealed that in both the mid-small-intestine contentsand mucosal samples multiple taxa (e.g., Bacteroidales, Clostridiales,Erysipelotrichales, Coriobacteriales) were differentially abundant (onthe absolute scale) among the four experimental groups of mice (FIG. 5).These results are in agreement with data previously obtained using atwo-step approach (utilizing absolute 16S rRNA gene copy quantificationwith a dedicated qPCR assay performed separately from the barcoding PCRreaction) described in [1, 2].

Example 5: General Protocols for Detecting Absolute AbundanceMeasurements of Mucosal and Luminal Microbial Communities

Absolute quantification of mucosal and luminal microbial community wasdetected according to an exemplary method herein described whereinabsolute abundance of sample 16S rRNAs was performed by digital PCR.

In particular, samples were collected from mice and the related DNAextracted All animal husbandry and experiments were approved by theCaltech Institutional Animal Care and Use Committee (IACUC protocols#1646 and #1769). Male and female germ free (GF) C57BL/6J mice were bredin the Animal Research Facility at Caltech, and 4-week-old femalespecific-pathogen-free (SPF) Swiss Webster mice were obtained fromTaconic Farms (Germantown, N.Y., USA). Mice were housed on heat-treatedhardwood chip bedding (Aspen Chip Bedding, Northeastern Products,Warrensburg, N.Y., USA) and provided with tissue paper (Kleenex,Kimberly-Clark, Irving, Tex., USA) nesting material. Experimentalanimals were fed standard chow (Lab Diet 5010), 6:1 ketogenic diet(Envigo TD.07797, Indianapolis, Ind., USA; FIG. 24) or vitamin- andmineral-matched control diet (Envigo TD.150300; FIG. 24). Diet designand experimental setup were taken from a recently published study [8].

To minimize cage effects, mice were housed two per cage with three cagesper diet group. Custom feeders, tin containers approximately 2.5 inchestall with a 1-inch diameter hole in the top, were used for the ketogenicdiet as it is a paste at room temperature. Autoclaved water was providedad libitum and cages were subjected to a daily 13:11 light:dark cyclethroughout the study. Mice were euthanized via CO2 inhalation asapproved by the Caltech IACUC in accordance with the American VeterinaryMedical Association Guidelines on Euthanasia [106].

The mock microbial community (Zymobiomics Microbial Community Standard;D6300) was obtained from Zymo Research (Irvine, Calif., USA). Thiscommunity is stored in DNA/RNA Shield, which could interfere withextraction efficiency at high concentrations. It was found that a 100 μLinput of a 10× dilution of the microbial community stock is the maximuminput that the Qiagen DNeasy Powersoil Pro Kit can handle withoutrecovery losses. Negative control blanks were also used which included100 μL of nuclease free water instead of mock community.

Fresh stool samples were collected immediately after defecation fromindividual mice and all collection occurred at approximately the sametime of day. For intestinal samples, the GIT was excised from thestomach to the anus. Contents from each region of the intestine(stomach, upper half of SI, lower half of SI, cecum, and colon) werecollected by longitudinally opening each segment with a scalpel andremoving the content with forceps. Terminal colonic pellets are referredto as stool. After contents were removed the intestinal tissue waswashed by vigorously shaking in cold sterile saline. The washed tissuewas placed in a sterile petri dish and then dabbed dry with a Kimwipe(VWR, Brisbane, Calif., USA) before scraping the surface of the tissuewith a sterile glass slide. These scrapings were collected as the mucosasamples. All samples were stored at −80° C. after cleaning and beforeextraction of DNA.

DNA was extracted from all samples by following the Qiagen DNeasyPowersoil Pro Kit protocol (Qiagen; Valencia, Calif., USA). Bead-beatingwas performed with a Mini-BeadBeater (BioSpec, Bartlesville, Okla., USA)for 4 min. To ensure extraction columns were not overloaded, we used ˜10mg of scrapings and ˜50 mg of contents. Half of the lysed volume wasloaded onto the column and elution volume was 100 μL. Nanodrop (NanoDrop2000, ThermoFisher Scientific) measurements were performed with 2 μL ofextracted DNA to ensure concentrations were not close to the extractioncolumn maximum binding capacity (20 μg).

The absolute abundance of the microorganism from the sample was detectedby digital PCR. The concentration of total 16S rRNA gene copies persample was measured using the Bio-Rad QX200 droplet dPCR system (Bio-RadLaboratories, Hercules, Calif., USA). The concentration of thecomponents in the dPCR mix used in this study were as follows: 1×EvaGreen Droplet Generation Mix (Bio-Rad), 500 nM forward primer, and500 nM reverse primer.

Universal primers to calculate the total 16S rRNA gene concentrationswere a modification to the standard 515F-806R primers [18] to reducehost mitochondrial rRNA gene amplification in mucosal andsmall-intestine samples (FIG. 27) [1, 2, 17]. Thermocycling foruniversal primers was performed as follows: 95° C. for 5 min, 40 cyclesof 95° C. for 30 s, 52° C. for 30 s, and 68° C. for 30 s, with a dyestabilization step of 4° C. for 5 min and 90° C. for 5 min. All ramprates were 2° C. per second.

The concentration of taxon-specific gene copies per sample was measuredusing a similar dPCR protocol, except with different annealingtemperatures. Annealing temperatures during thermocycling fortaxa-specific primers can be found in FIG. 27. The concentration of thecomponents in the qPCR mix used in this study were as follows: 1×SsoFast EvaGreen Supermix (BioRad), 500 nM forward primer, and 500 nMreverse primer. Thermocycling was performed as follows: 95° C. for 3min, 40 cycles of 95° C. for 15 s, 52° C. for 30 s, and 68° C. for 30 s.All dPCR measurements are single replicates.

Concentrations of 16S rRNA gene per microliter of extraction werecorrected for elution volume and losses during extraction beforenormalizing to the input sample mass (Equation 1).

$\begin{matrix}{{{Microbial}\mspace{14mu} {Load}} = {{dPCR}\mspace{14mu} {concentration}*{elution}\mspace{14mu} {volume}*\frac{{dead}\mspace{14mu} {volume}}{{extraction}\mspace{14mu} {volume}}*\frac{1}{{sample}\mspace{14mu} {mass}}}} & (1)\end{matrix}$

Absolute abundance of individual taxa was calculated either by dPCR withtaxa-specific primers or multiplying the total microbial load fromEquation 1 by the relative abundance from 16S rRNA gene ampliconsequencing.

16S rRNA Gene Amplicon Sequencing was then performed. In particular,extracted DNA was amplified and sequenced using barcoded universalprimers and protocol modified to reduce amplification of host DNA [1, 2,17]. The variable 4 (V4) region of the 16S rRNA gene was amplified intriplicate with the following PCR reaction components: 1×5Prime Hotstartmastermix, 1× Evagreen, 500 nM forward and reverse primers. Inputtemplate concentration varied. Amplification was monitored in a CFX96RT-PCR machine (Bio-Rad) and samples were removed once fluorescencemeasurements reached ˜10,000 RFU (late exponential phase).

Cycling conditions were as follows: 94° C. for 3 min, up to 40 cycles of94° C. for 45 s, 54° C. for 60 s, and 72° C. for 90 s. Triplicatereactions that amplified were pooled together and quantified with Kapalibrary quantification kit (Kapa Biosystems, KK4824, Wilmington, Mass.,USA) before equimolar sample mixing. Libraries were concentrated andcleaned using AMPureXP beads (Beckman Coulter, Brea, Calif., USA). Thefinal library was quantified using a High Sensitivity D1000 TapestationChip. Sequencing was performed by Fulgent Genetics (Temple City, Calif.,USA) using the Illumina MiSeq platform and 2×300 bp reagent kit forpaired-end sequencing.

16S rRNA Gene Amplicon Data Processing was then performed: Processing ofall sequencing data was performed using QIIME 2 2019.1 [80]. Rawsequence data were demultiplexed and quality filtered using the q2-demuxplugin followed by denoising with DADA2 [3]. Chimeric read countestimates were estimated using DADA2. Beta-diversity metrics (Aitchisondistance [107], Bray-Curtis Dissimilarity) were estimated using theq2-diversity plugin after samples were rarefied to the maximum number ofsequences in each of the relevant samples. Rarefaction was used to forcezeros in the dataset to have the same probability (across samples) ofarising from the taxon being at an abundance below the limit ofdetection.

Although rarefaction may lower the statistical power of a dataset [108]it helps decrease biases caused by different sequencing depths acrosssamples [109]. Taxonomy was assigned to amplicon sequence variants(ASVs) using the q2-feature-classifier [110] classify-sklearn naïveBayes taxonomy classifier against the Silva [56] 132 99% OTUs referencesfrom the 515F/806R region. All datasets were collapsed to the genuslevel before downstream analyses. All downstream analyses were performedin IPython primarily through use of the Pandas, Numpy and Scikit-learnlibraries.

Data Transforms and Dimensionality Reduction was then performed: Fordimensionality reduction techniques requiring a log transform, apseudo-count of 1 read was added to all taxa. With relative abundancedata, the centered log-ratio transform was used (Equation 2) to handlecompositional effects whereas a log transform was applied to theabsolute-abundance data to handle heteroscedasticity in the data.

$\begin{matrix}{x_{clr} = {{\lbrack {{\log ( \frac{x_{1}}{G(x)} )},{\log ( \frac{x_{2}}{G(X)} )},\ldots \mspace{14mu},{\log ( \frac{x_{D}}{G(X)} )}} \rbrack \mspace{14mu} {where}\mspace{14mu} {G(X)}} = \sqrt[D]{x_{1}*x_{2}*\ldots*x_{D}}}} & (2)\end{matrix}$

For comparative purposes, principal co-ordinates analysis (PCoA) wasalso performed using the Bray-Curtis dissimilarity metric. Principalcomponent analysis (PCA) and PCoA were performed using scikit-learndecomposition methods. Feature loadings for each principal componentwere calculated by multiplying each eigenvector by the square root ofits corresponding eigenvalue. All data were visualized using matplotliband seaborn.

Taxa Limits of Quantification were then determined: Poisson confidenceintervals were calculated by bootstrapping Poisson samples for rateparameters across the percentage abundance range (0-1) corresponding toeither the input DNA copies or number of reads. to 10⁴ bootstrapreplicates were taken with a Poisson sample size of 4 to match thenumber of replicates we sequenced. The % CV for each replicate wascalculated and the middle 95^(th) percentile was shown as the confidenceinterval.

Thresholds for percentage abundance were calculated by first fitting anegative exponential curve y=ax^(−b) to the plot of % CV versuspercentage abundance using SciPy. Then the percentage abundance at agiven % CV threshold was determined. This process was repeated aftersubsampling the data at decreasing read depths to find the relationshipbetween percent abundance accuracy limits at sequencing depth.

When measuring the absolute abundance of a given taxon in a sample, manyfactors contribute to the uncertainty of the measurement. Two primaryfactors, extraction efficiency and average amplification efficiency foreach taxon, should be equivalent for each taxon across samples processedunder identical conditions and thus neither should impact the discoveryof differential taxa. However, other factors contributing to theuncertainty of an absolute-abundance measurement vary among samples andcan impact the discovery of differential taxa.

At least six independent errors can contribute to the overalluncertainty of a taxon's absolute abundance: (i) extraction error (ii)the Poisson sampling error of dPCR, (iii) the Poisson sampling error ofsample input into an amplification reaction to make a sequencinglibrary, (iv) the uncertainty in the amplification rates amongsequences, (v) the Poisson sampling error of the sequencing machine, and(vi) the uncertainty in taxonomic assignment resulting from differentsoftware programs that differ in how they convert raw sequencing readsto a table of read counts per taxon.

To measure the total error in our absolute-abundance measurements, wecompared the true absolute load value of four “representative” taxa(taxa that are common gut flora from different taxonomic ranks) asmeasured by taxa-specific dPCR, with the value obtained from our methodof quantitative sequencing with dPCR anchoring (FIG. 9, panel b) andthen analyzed the relative error in these measurements, defined as thelog 2 of the observed taxon load over the true taxon load. Applicantconstructed a quantile-quantile (Q-Q) plot (FIG. 21) of themean-centered log 2 relative errors and found that the errors appearednormally distributed.

This was confirmed by running a Shapiro-Wilk test (P-value=0.272) on themean-centered log 2 relative errors, which uses a null hypothesis thatthe dataset comes from a normal distribution. The standard deviation ofthe mean-centered log₂ relative errors was 0.48, which results in a 95%confidence interval of ˜(−1,1),indicating a 2× precision on eachindividual measurement. However, as seen with Akkermansia(g) (FIG. 9,panel b), accuracy offsets may exist for specific taxa. It is importantto note that all samples used in this analysis had relative abundancesabove the 50% CV threshold defined in FIG. 8, panel d and thus noconclusions were made about the precision of absolute abundancemeasurements for taxa with relative abundances below the 50% CVthreshold.

When measuring the absolute abundance of a taxon from a definedpopulation (e.g., healthy adults, mice on a ketogenic diet) it isunlikely this abundance comes from a well-defined statisticaldistribution. Given this inherent limitation, non-parametric statisticaltests were used, which do not rely on distributional assumptions, forour differential abundance analyses.

Statistical comparisons between diet groups were analyzed using theKruskal-Wallis [111] rank sums test with Benjamini-Hochberg [112]multiple hypothesis testing correction. All statistical tests wereimplemented using SciPy.stats Kruskal function andstatsmodels.stats.multitest multipletests function with the fdr_bhoption for Benjamini-Hochberg multiple-testing correction. Whencalculating differentially abundant taxa, only taxa present in at least4 out of 6 mice in a group were considered to remove fold-changeoutliers when plotting (FIG. 12, panels a-b).

Samples were separated by diet (ketogenic and control) and only stoolsamples were used (days 4, 7, and 10). The total microbial load and top30 taxa with the highest average absolute abundance were selected foranalysis. Spearman's rank correlation coefficient and correspondingP-values were calculated for all pairwise interactions using thescipy.stats.spearmanr function. Benjamini-Hochberg procedure was tocalculate q-values, which account for multiple hypothesis testing. Aheatmap of the diagonal correlation matrix was plotted (FIG. 20) forq-values <10% FDR.

The complete sequencing data generated during this study are availablein the National Center for Biotechnology Information Sequence ReadArchive repository under study accession number PRJNA575097. Raw datafor all figures available through CaltechDATA:data.caltech.edu/records/1371. Raw data for all figures is also providedas source data files.

The impact of extraction procedures, 16S rRNA sequencing and digitalPCR, on the quantification of the 16S rRNA were tested with experimentsdescribed in Examples 6-8. The accuracy of the quantification performedwith methods of the disclosure with respect to other approaches and indifferential taxon analysis was tested with experiments described inExamples 9 and 10.

Example 6: Efficient DNA Extraction Across Microbial Loads and SampleTypes

To estimate the maximum quantity of sample that could be extractedbefore overloading the 20-μg column capacity, total DNA and microbialDNA load were measured across small intestine and large intestinelumenal and mucosal samples (FIG. 13).

Extraction efficiency was then evaluated across three tissue matrices(mucosa, cecum contents, and stool) to assess whether variation inlevels of PCR inhibitors and non-microbial DNA interfered with microbialquantification. A defined 8-member microbial community was spiked intoGI samples taken from germ-free (GF) mice.

To assess quantitative limits, a dilution series of microbial spike-inwere performed from 1.4×10⁹ CFU/mL to 1.4×10⁵ CFU/mL. dPCRquantification showed near equal and complete recovery of microbial DNAover 5 orders of magnitude (FIG. 8, panel a). Overall, Applicantmeasured˜2× accuracy in extraction across all tissue types (cecumcontents, stool, SI mucosa) when total 16S rRNA gene input was greaterthan 8.3×10⁴ copies (FIG. 14). Normalizing this sample input to theapproximate maximum extraction mass (200 mg stool, 8 mg mucosa) yieldeda lower limit of quantification (LLOQ) of 4.2×10⁵ 16S rRNA gene copiesper gram for stool/cecum contents and 1×10⁷ 16S rRNA gene copies pergram for mucosa. Mucosal samples had a higher LLOQ because the high hostDNA in this tissue type saturates the column, limiting total mass input.

Next, to ensure extraction performance was consistent for bothGram-negative and Gram-positive microbes, Applicant performed 16S rRNAgene amplicon sequencing using previously described improved primers andprotocol [1, 2, 17] on a subset of the extracted samples (FIG. 8, panelb). It is important to note that all amplification reactions for 16SrRNA gene library prep were monitored with real-time qPCR and thereactions were stopped when they reached the late exponential phase tolimit overamplification and chimera formation [1, 2, 17, 76, 113, 114].Extraction appeared less even among microbial taxa at lower totalmicrobial DNA inputs (FIG. 8, panel b).

This discrepancy from the theoretical profile did not correlate with thepresence of chimeric sequences (FIG. 15) and was likely a function ofthe reduced accuracy incurred when diluting complex microbial samples.Additionally, sequencing samples with low total microbial loads (<1×10⁴16S rRNA gene copies) resulted in the presence of contaminants, asconfirmed by sequencing of negative-control extractions (FIG. 22).

FIG. 22 lists Contaminant taxa with greater than 1% abundance innegative-control extraction.

Example 7: Quantitative Limits of 16S rRNA Gene Amplicon Sequencing

The impact of the 16S rRNA gene amplicon sequencing on the 16S rRNAquantification was tested with the following experiments.

In particular to establish the precision of relative-abundancemeasurements, four replicates of DNA extractions from cecum samples weresequenced. Libraries from one DNA extraction were prepared with eitheran input of 1.2×10⁷ 16S rRNA gene copies or 1.2×10⁴ 16S rRNA gene copiesto determine the impact of starting DNA amount on sequencingvariability.

The coefficient of variation (% CV) was calculated for each taxon'srelative abundance from amplicon sequencing the replicate samples. Eachtaxon's mean relative abundance (n=4) was then plotted against itscorresponding coefficient of variation of the relative abundance (FIG.8, panel c). “dropouts” were defined as taxa present only in thehigh-DNA-input sample whereas “contaminants” were defined as taxapresent only in the low-DNA-input sample.

The two dropout taxa in the low input sample corresponded to the lowestabundance taxa from the high input DNA sample (markers with an “x”, FIG.8, panel c). Most of the contaminant taxa had a relative abundance<0.03%, but three taxa (Pseudomonas(g), Acinetobacter(g),Rhizobiales(f)) had relative abundances of 0.38%, 0.35%, and 0.1%,respectively. These three taxa were also the three most commoncontaminants in our negative-control extractions (FIG. 22).

The presence of contaminants in the sample containing 1.4×10⁴ 16S rRNAgene copies was consistent with the input amount at which we observedcontaminants in the mixed microbial community dilutions (FIG. 8, panelb). A bootstrapped Poisson sampling confidence interval was calculatedat our sequencing depth (28,000 reads) to assess how close the accuracylimits were to the theoretical limits (shading, FIG. 8, panel c). At thelow DNA input level of 1.2×10⁴ 16S rRNA gene copies, Applicant began toreach the fundamental Poisson loading limit in the library-preparationreaction (FIG. 16, panel a).

A divergence of the % CV at ˜0.01% abundance was expected because at aread depth of 28,000 a relative abundance of 0.01% is a measure of ˜3reads whereas at a total 16S rRNA gene copy input of 1.4×10⁴ a relativeabundance of 0.01% is ˜1 copy. Poisson statistics also helped us definethe theoretical lower limits of relative-abundance measurements as afactor of sequencing depth (FIG. 16, panel b).

Next an approximate threshold was quantified, which would tell us, for agiven sequencing depth, at what percentage of relative abundance onelose accuracy in the measurements (this threshold was defined as“relative abundance threshold”). To determine this threshold, a negativeexponential was fit to the replicate data and the percentage abundancewas identified at which 30% CV was observed. This threshold is afunction of the sequencing depth, so Applicant subsampled the data atdecreasing read counts and repeated the exponential fitting method tocalculate the relationship between the relative abundance threshold andsequencing depth (FIG. 8, panel d). Greater sequencing depths yieldedlower quantitative limits with diminishing returns, as expected.Applicant found that the threshold for percentage abundance decreaseswith increasing sequencing depth with a square root dependence analogousto the square-root dependence of Poisson noise. This trend follows for %CV thresholds of 40% and 50% as well (FIG. 8, panel d).

This analysis provides a framework with which to impose thresholds onrelative-abundance data that are grounded on the calculated limits ofquantitation.

Example 8: Absolute Quantification of Taxa Via Digital PCR (dPCR)Anchoring

Absolute abundances of taxa were determined from sequencing data usingdPCR measurement of total microbial loads as an anchor. The accuracy ofquantification through dPCR was then tested by the followingexperiments.

Briefly, relative abundance of each taxon was measured by sequencing andthese numbers were multiplied by the total number of 16S rRNA genecopies (obtained using the same universal primers from ampliconsequencing, without the barcodes) from dPCR.

Next, the accuracy of this quantitative sequencing approach wasevaluated. Typically, evaluation of quantitative accuracy and precisionwould involve the use of a mock microbial community (like the one usedin FIG. 8).

However, because the absolute instead of relative abundances wascomputed, it is feasible to use the actual gut-microbiota samples andcompare the results to the dPCR data obtained with relevanttaxa-specific primers. The 16S rRNA gene copy amount was then normalizedto the mass of each extracted sample after correcting for volume losses(Equation 1 in Example 5).

Four representative taxa were selected to encompass common gut flora ofvarying classification levels: Akkermansia muciniphila(s),Lachnospiraceae(f), Bacteroidales(o), and Lactobacillaceae(f). Likeeubacterial primers, taxa-specific primer sets can (in principle) giverise to nonspecific amplification due to overlap with host mitochondrialDNA. To avoid nonspecific amplification, Applicant ran temperaturegradients with GF mucosal DNA and taxa-specific microbial DNA toidentify the optimal annealing temperature for each primer set (FIG.17).

Each taxa-specific primer targets a separate region of the 16S rRNA genethan the universal primer set, thus keeping the gene copy numberequivalent across primers. High correlation coefficients were observedbetween the taxa load determined by quantitative sequencing with dPCRanchoring and the taxa load measured by dPCR with taxa-specific primers(all r²>=0.97, FIG. 9, panel a) for all four taxa over a range of ˜6orders of magnitude. The ratio of the total load measurements obtainedby quantitative sequencing with dPCR anchoring and by dPCR withtaxa-specific primers showed unity agreement between three of the fourprimer sets with 2-fold deviation from the mean (FIG. 9, panel b andFIG. 21). Sequencing quantification was consistently 2.5-fold higherthan dPCR quantification for the species Akkermansia muciniphila (FIG.9, panel b).

Amplification bias as a factor cannot be confirmed because the error didnot depend on the number of cycles used in library preparation. Analternative factor could be a discrepancy in coverage/specificitybetween the taxon-specific and universal primer sets. Applicant nexttested the limits of the sequencing accuracy as a factor of input DNAload. A 10× dilution series of a cecum sample was created to cover inputDNA loads of 1×10⁸ copies down to 1×10⁴ copies.

Minimal differences in beta diversity (Aitchison distance) between theundiluted and diluted samples were observed with a trend towardsincreasing difference with decreasing DNA load (FIG. 9, panel c). Thisnegative correlation between beta diversity and microbial load is notunexpected due to the higher presence of contaminant species from ournegative controls in the lower input samples (FIG. 8, panel b).

Example 9: Absolute Vs Relative Abundance Analysis in a Ketogenic-DietStudy

A ketogenic-diet study was performed to test the impact of using aquantitative framework for 16S rRNA gene amplicon sequencing.

In particular, the goals of this study were twofold. First, Applicantwished to test whether absolute instead of relative microbial abundancescan more accurately quantify changes in microbial taxa between studygroups. Second, Applicant wished to investigate how using a quantitativesequencing framework can guide the interpretation of changes in taxaacross study conditions. The objective was not to make claims about theeffect of a ketogenic diet on the microbiome, but rather to use thismodel as an illustration of the added benefits of using thisquantitative sequencing framework.

After one week on a standard chow diet, 4-week old Swiss Webster micewere split into two groups (n=6 each): one was fed a ketogenic diet andthe other a vitamin and mineral matched control diet (FIG. 24). Stoolwas sampled immediately before the two diets were introduced (day 0),and again at days 4, 7 and 10. Additionally, on day 10, all mice wereeuthanized and lumenal and mucosal samples were collected fromthroughout the GI tract (FIG. 10, panel a). Microbial loads (quantifiedwith dPCR) ranged from ˜10⁹ 16S rRNA gene copies/g in small intestinalmucosa to 10¹² 16S rRNA gene copies/g in stool. On average, we observedlower microbial DNA loads in the mice on the ketogenic diet comparedwith mice on the control diet, except in the stomach, where loads weresimilar in mice on both diets (FIG. 10, panel b).

All stool samples and roughly half of the samples for all other GI sites(evenly distributed across mice on the two diets) underwent 16S rRNAgene amplicon sequencing. Ordination methods (PCA, PCoA, NMDS, etc.) area common exploratory data analysis technique in the microbiome field.Common transformation techniques based on non-Euclidian distances (e.g.,Bray-Curtis, UniFrac) can skew the accuracy of visualizations ofrelative data (FIG. 18, panel a) [115]. The centered log-ratiotransformation (CLR, often used to compute the Aitchison distance) wasused to handle compositional effects, and performed PCA on thetransformed absolute abundance data for all samples from the finalcollection day (FIG. 10, panel c). A clear separation along the firsttwo principal components (PC) was observed. Separation along PC1 wasrelated to the location within the GI tract whereas separation along PC2was related to the diet. The PCA analysis suggested that stomach sampleswere distributed somewhere in-between small-intestine andlarge-intestine samples, possibly resulting from coprophagy in mice [1,2]. Additionally, the mucosal and lumenal samples from the smallintestine on the control diet seemed to be closer together than on theketogenic diet (FIG. 10, panel c).

Next, calculations were performed to investigate which taxa werecontributing to separation in our principal component space. Applicantcalculated the scaled covariance between each taxon and the first twoprincipal components by multiplying the eigenvectors by the square rootof their corresponding eigenvalues. These values are also known as“feature loadings.” Plotting these feature loadings from smallest tohighest shows that Lactobacillus(g) and Lactococcus(g) had the greatestimpact on separation along PC in the direction of the small intestinalsamples whereas RuminiClostridium(g) and Lachnospiraceae(f) separated inthe direction of the large intestine (FIG. 10, panel d). This matcheswith what we know about the major genera commonly present in the smalland large intestine [116]. Along PC2 (the “diet axis”), the top twocontributing taxa towards the control diet were Turicibacter(g) andMarvinbryantia(g), while towards the ketogenic diet Akkermansia(g) andEnterococcus(g) had the greatest covariance.

Although the CLR transformation preserves distances in principalcomponent space regardless of whether the starting data are relative orabsolute, it normalizes out the differences in total loads by looking atlog ratios between each taxon's abundance and the geometric mean of thesample (FIG. 18, panel b). In many cases, one wants to know if theabsolute load of a taxon is higher or lower under different conditions(e.g., in mice on ketogenic and control diets). When the total microbialload varies among samples, analyses of relative abundance cannotdetermine which taxa are differentially abundant (FIG. 7).

To assess the impact of using absolute quantification in analyses,microbiomes of stool samples from mice on ketogenic and control dietswere analyzed. PCA analysis on the CLR-transformed relative abundancesof microbial taxa showed separation between the two diets (FIG. 11,panel a). Feature loadings were analyzed as before, but this time totalimpact of each taxa on the PC space was plotted, which was defined asthe sum of the feature loading vectors in PC1 and PC2 (FIG. 11, panelb). The same analysis was performed on the log-transformed absoluteabundance data (FIG. 11, panel a). Separation between diets is clear inboth relative and absolute abundance analyses, but the contribution ofeach taxon to the separation differed in direction and magnitude.Comparing the magnitude of feature loadings for two taxa, Akkermansia(g)and Acetatifactor(g), between the relative and absolute PCA plots showedobvious differences in the contribution of a given taxa to theseparation in principal-component space. Analysis of relative-abundancedata implies that Akkermansia(g) has the biggest contribution onseparation between diets in PC space whereas the absolute abundance dataimplies that ˜50% of the taxa in the sample have a greater contributionthan Akkermansia(g) to the separation between the diets in PC space.

PCA is only an exploratory data-analysis technique, so next Applicantused a non-parametric statistical test to test for differentiallyabundant taxa in stool samples from mice on control and ketogenic diets(FIG. 11, panel c) [111]. Separate analyses of the relative and absoluteabundance data were performed. The −log 10 P-value was plotted for eachtaxon's relative abundances against the corresponding −log 10 P-valuefor that taxon's absolute abundances. Points along the diagonal indicatecongruence between the predictions from the relative and absoluteabundance data. Points in the upper left corner indicate taxa thatdiffered between the diets in the analysis of relative abundance but notin the analysis of absolute abundance. Conversely, points in the lowerright corner indicate taxa that do not differ between diets in theanalysis of relative abundance but do differ in the analysis of absoluteabundance. Akkermansia(g) is an example of a microbe that appears todiffer (P=6.49×10⁻³, Kruskal-Wallis) between mice on the two diets inthe relative-abundance analysis but not in the absolute-abundanceanalysis (P=3.37×10⁻¹, Kruskal-Wallis). Lachnospiraceae(f) showed theopposite trend; in the relative-abundance analysis it appears unchanged(P=6.31×10⁻¹, Kruskal-Wallis) but in the absolute-abundance analysis itdiffers (P=3.95×10⁻³, Kruskal-Wallis) between the two diets.

Neither of these analyses is wrong, they are simply asking two differentquestions: with relative data, the question is whether the percentage ofthat microbe is different between two conditions whereas with absolutedata, the question is whether the abundance of that microbe is differentbetween two conditions.

To explore one example of how different interpretations of how taxadiffer between study conditions occur when using relative versusabsolute abundance, Applicant analyzed Akkermansia(g) in stool acrosseach of the three time points on experimental diets (days 4, 7, and 10)and day 0 on chow diet. For simplicity in this illustration, we compareddata from one mouse on each diet, but the trends hold on average betweenall mice on the two diets (FIG. 19). Analysis of relative microbialabundance demonstrated ˜3× higher abundance of Akkermansia(g) in samplesfrom the ketogenic compared with the control diet on days 7 and 10.

However, when analyzing the difference in absolute abundance, morenuanced conclusions emerged. The rise in Akkermansia(g) results fromswitching mice from chow to experimental diets. The resultingAkkermansia(g) loads are similar in the two diets on days 7 and 10.However, the ketogenic diet reduces the total microbial load relative toboth chow and control diets, therefore leading to the observed higher %of Akkermansia(g) in samples from mice on ketogenic diet.

Example 10: Absolute Abundances for Quantitative Differential TaxonAnalysis

A microbiota abundance was analyzed to test the accuracy of method ofthe disclosure in differential taxon analysis.

In particular, the absolute microbiota abundances in stool and lowersmall intestinal mucosa samples from day 10 was analyzed. A volcanoplot, akin to those used in gene expression studies, was used torepresent the overall changes in taxa abundances between the two diets,and the absolute abundance of each taxon was indicated by the size ofits symbol (FIG. 12, panel a). P-values from the Kruskal-Wallis testswere corrected for multiple hypothesis testing with theBenjamini-Hochberg method, resulting in q-values [111, 112]. A falsediscovery rate (FDR) of 10% was labeled on the volcano plot and q-values<0.1 were used as a cutoff for designating differential taxa fordownstream analyses.

Comparisons between the two GI locations showed substantial differencesin microbial response to diet by location. In stool, approximately 66%of the differential taxa were lower on the ketogenic diet vs the controldiet whereas in the lower SI mucosa, >80% of the differential taxa weremore abundant in the ketogenic diet than control diet (FIGS. 25-26).

Several specific differential taxa that were discordant between stooland lower SI mucosa were highlighted. (1) Bacteroides(g) was lower onketogenic diet in stool and higher on ketogenic diet in lower SI mucosa.This type of result could lead researchers who analyze stool samples tobelieve that lower levels of Bacteroides(g) may be associated with aphenotype when it could be the opposite if the phenotype is driven bythe SI mucosal microbiota. (2) Parabacteroides(g) and LachnospiraceaeGCA-900066575 (g) showed the highest fold changes (in oppositedirections) in stool but were not detected in the lower SI mucosa. Theopposite was observed for Escherichia(g), which was more abundant in theketogenic diet than the control diet in the lower SI mucosa but was notdetected in stool. (3) Akkermansia(g) and Desulfovibrionaceae(f) weremore abundant in the ketogenic diet than the control diet in the lowerSI mucosa but were similar between the two diets in stool. Such microbescould have a relationship with phenotype through the small intestine butwould be missed if only stool samples are analyzed.

A further breakdown of the differential taxa, using the quantitativelimits of sequencing accuracy (defined earlier), allowed one tocategorize four distinct scenarios that describe how microbes differedbetween GI locations of mice on the two diets. We refer to these fourscenarios as “quantification classes” (FIG. 12, panel b).

First, there were microbes that were present in one diet and absent inthe other (“presence/absence” class). For example, Dorea(g), in stool,and Escherichia(g), in SI mucosa, were absent from the control diet butpresent in the ketogenic diet.

Second, there were microbes above the detection limit but below thequantitative limit in both diets (“no quant” class). For example, instool, Candidatus Soleaferrea(g), ranges in relative abundance from0.002% to 0.025%, well below the 30% CV quantification threshold of0.04% (as defined in FIG. 8, panel d). Thus, the difference of thismicrobe between mice on the two diets cannot be quantitatively defined.

Third, microbes were above the detection limit in both diets but onlyabove the quantitative limit in one of the diets (“semi-quant” class).For example, Desulfovibrionaceae(f) in the lower small-intestine mucosawas above the detection limit in mice on both diets but only above thequantitative limit in mice on the ketogenic-diet, so although one can beconfident that a difference between the diets exists, one cannot beconfident in the measurement of the magnitude of that difference.

Fourth, microbes were found above the quantitative limits in both diets(“quant” class). For example, for Parabacteroides(g) in stool, theresults supported confidence in both the difference between the diets(it was more abundant in the control diet) and in the magnitude of thatdifference (a 32.2-fold difference). In particular, the lowestconfidence was found in the measured absolute fold change of a taxonthat is classified in the presence/absence class, and the greatestconfidence in a taxon in the quant class.

Example 11: General Protocols for Absolute Quantification of Target 16SrRNA Following Modification Microbiota by Preventing Self-Reinoculationwith Fecal Flora in Mice

Absolute quantification of microbial community in mice followingmodification of the microbiota performed by preventing selfre-inoculation was detected according to an exemplary method hereindescribed.

All animal handling and procedures were performed in accordance with theCalifornia Institute of Technology (Caltech) Institutional Animal Careand Use Committee (IACUC). C57BL/6J male specific-pathogen-free (SPF)mice were obtained at the age of 7-8 weeks from Jackson Laboratory(Sacramento, Calif., USA) and housed four mice per cage. Two cohorts ofanimals were used: the first cohort was allowed to acclimate in theCaltech animal facility for 2 months and mice were 4 months old at thestart of the study; the second cohort acclimated for 6 months and micewere 8 months old at the start of the study.

All animals were maintained on chow diet (PicoLab Rodent Diet 20 5053,LabDiet, St. Louis, Mo., USA) and autoclaved water ad lib and subjectedto a daily 13:11 light:dark cycle during acclimation and throughout theentire study. Mice were given measured amounts of food, and food intakeduring the experiment was measured by weighing the food during weeklycage changes and at the end time point for each animal. Body weight wasmeasured at the start of the experiment, during weekly cage changes, andat the end time point.

During the experiment, all mice were singly housed in autoclaved cages(Super Mouse 750, Lab Products, Seaford, Del., USA). The mice in thecontrol (CTRL), mock tail cup (TC-M) and functional tail cup (TC-F)treatments were housed on heat-treated hardwood chip bedding (Aspen ChipBedding, Northeastern Products, Warrensburg, N.Y., USA) and providedwith tissue paper (Kleenex, Kimberly-Clark, Irving, Tex., USA) nestingmaterial. The mice in the wire-floor (WF) treatment were housed onraised wire floors with a mesh size of 3×3 per square inch (#75016, LabProducts) and provided with floorless paper huts (#91291, ShepherdSpecialty Papers, Watertown, Tenn., USA). A thin layer of woodchipbedding was added under the wire floors to absorb liquid waste from theanimals (FIG. 33, panel D).

The tail cups were designed based on published literature [92-94, 117,118], including the locking mechanism [117]. Each cup was locked inplace around the hind end of animals by anchoring to a tail sleevedesigned with a perpendicular groove. Such tail sleeves allow for thecup to be held snugly against the animal so that the total weight of thetail cup is distributed along a large surface area of the tail skin,which minimizes complications. When mounted, the tail cups can freelyrotate along the longitudinal axis, which ensures the locking mechanismdoes not strangulate the tail.

The tail cups were hand-made from 20 mL syringes (#4200.000V0 Norm-Ject20 mL Luer-Lock, Henke-Sass Wolf GmbH, Tuttlingen, Germany) as depictedon FIG. 33, panels A-C. Multiple perforations were designed toaccelerate desiccation of the captured fecal pellets. Lateral slitsallowed for increasing the diameter of the locking edge; pressing on theslits with two fingers allowed tail cups to be quickly unfastened fromtail sleeves. Mock tail cups were modified with wide gaps in the wallsto allow the fecal pellets to fall out of the cup.

To prevent mice from gnawing on the plastic parts of the tail cups(which could create a jagged edge and lead to a subsequent injury), theywere reinforced with metal flared rings made from stainless steelgrommets (#72890, SS-4, C.S. Osborne, Harrison, N.J., USA) that weremodified to reduce their size and weight. Metal rings were attached totail cups using 4 mm-wide rubber rings cut from latex tubing (AmberLatex Rubber Tubing #62996-688, ½″ ID, ¾″ OD; VWR, Radnor, Pa., USA).

Tail sleeves were made from high-purity silicone tubing (HelixMark60-411-51, ⅛″ ID, ¼″ OD; Helix Medical, Carpinteria, Calif., USA). Thetubing was split longitudinally and a 2.0 mm wide strip of the wall wasremoved to accommodate for variable tail diameters among animals andalong the tail length, to prevent uneven tail compression, and tofacilitate uniform application of the tissue adhesive. The perpendiculartail-cup mounting groove was made using a rotary tool (Craftsman#572.610530, Stanley Black & Decker, New Britain, Conn., USA) equippedwith a cutting disc (RD1, Perma-Grit Tools, Lincolnshire, UK). Each tailcup and sleeve together weighed approximately 4.12 g empty.

Before mounting the tail cups, animals were anesthetized with 10 minisoflurane and placed on a heating pad to maintain body temperature.Sleeves were de-greased on the inside using 70% ethanol and a veterinarytissue adhesive (GLUture Topical Adhesive #32046, Abbott Laboratories,Lake Bluff, Ill., USA) was applied to the tail base. The adhesive wasallowed to cure for 5 min and then tail cups were mounted. Mice werereturned back to their cages and allowed to recover from the anesthesiaand ambulate.

Tail cups were emptied of fecal pellets daily at 08:00 AM. Mice wereprompted to enter a restrainer [119] made from a black polypropylene 50mL conical tube (TB5000 LiteSafe, Cole-Parmer, Vernon Hills, Ill., USA)and the tail cups were unclipped and quickly emptied. Any residue on thetail cup was cleaned using a paper towel and Rescue solution (ViroxTechnologies, Oakville, ON, Canada) prior to the cups being remounted.Animals fitted with the mock tail cups were subjected to the identicalprocedure to match the handling conditions.

Tail cups were mounted in animals for a duration of between 12 and 20days. All TC-F animals were time-matched with TC-M animals, (i.e., eachanimal from the TC-F group had a time-matched animal from the TC-M grouphandled and euthanized at the same time).

All mice were euthanized as approved by the Caltech IACUC in accordancewith the American Veterinary Medical Association Guidelines onEuthanasia [106]. Mice were euthanized while under isoflurane anesthesia(delivered via a calibrated gas vaporizer in an induction chamberfollowed by maintenance on a nose cone) via cardiac puncture followed bycervical dislocation. Blood was collected using a 1 mL syringe (#309659,Becton Dickinson) and 21G×1″ needle (#26414, EXELINT International,Redondo Beach, Calif., USA).

Blood was immediately placed into K₂EDTA plasma separation tubes(MiniCollect 450480, Greiner Bio-One GmbH, Kremsmunster, Austria),gently mixed, and stored on ice for up 1 h prior to centrifugation. Bileand urine were collected directly from the gall and urinary bladdersrespectively using a 1-mL syringe (#4010.200V0 Norm-Ject 1 mL TuberculinLuer, Henke-Sass Wolf GmbH) and 27G×½″ needle (#26400, EXELINTInternational) and stored on ice.

Fecal samples were collected if present at the time of euthanasia. Theentire gastrointestinal tract was excised from the gastro-esophagealjunction to the anal sphincter and stored on ice during processing.

Blood samples were centrifuged in the plasma separation tubes at 2000RCF for 5 min at 4° C. Plasma was separated and stored at −80° C.

To prepare samples for the main experimental analyses (FIGS. 29-31),each mouse GIT was split into stomach, three equal-length thirds of thesmall intestine, cecum, and colon. Contents from each segment of the GITwere flushed out using 2-5 mL of cold (4° C.) sterile autoclaved salinesolution (0.9% NaCl (#S5886, Sigma-Aldrich) in ultrapure water (Milli-Q,MilliporeSigma, Burlington, Mass., USA)) followed by very gentlesqueezing with tweezers to avoid mucosal damage. All samples were storedon ice during processing.

An aliquot of each sample diluted in saline was concentrated bycentrifugation at 25000 RCF for 10 min at 4*C. The supernatant wasremoved and the pellet was reconstituted in 9 volumes of 1× DNA/RNAShield (DRS) solution (R1100-250, Zymo Research, Irvine, Calif., USA),mixed by vortexing and stored at −80° C. for future DNA extraction.Separate aliquots of each sample were stored at −80° C. for themetabolomic (bile acid) analysis.

Preparation of GIT contents for the MPN-based microbial quantificationand 16S rRNA gene amplicon sequencing (pilot study; FIG. 36, panel B)was the same as above, but conducted inside a vinyl anaerobic chamber(Coy Laboratory Products, Grass Lake, Mich., USA) in an atmosphere of 5%hydrogen, 10% carbon dioxide, and 85% nitrogen. All samples weremaintained on ice and immediately processed for the culture-based assay.

After flushing its contents, each segment of the GIT was gently rinsedin sterile cold (˜4° C.) saline, cut longitudinally, and placed flat ona glass slide. The mucosa was scraped from the tissue gently using asecond clean glass slide. Glass slides (VistaVision #16004-422, VWR)were sterilized by dry heat sterilization at 200° C. for at least 2 h.Mucosal scrapings were collected and combined with 9 volumes of DRSsolution, mixed by vortexing, and stored at −80° C. in preparation forDNA and RNA extraction.

A Most Probable Number (MPN) assay was then performed. For the pilotstudy (FIG. 36, panel A), the MPN assays (adapted from [120-124]) wereperformed on each GIT section (stomach, three sub-sections of the smallintestine, cecum, and colon) from five mice fitted with functional tailcups and five control mice. The growth medium was brain-heart infusionbroth (Bacto BHI, #237500, Becton Dickinson, Franklin Lakes, N.J., USA),prepared in ultrapure water (Milli-Q), sterilized by autoclaving,allowed to cool to room temperature, and supplemented with 1.0 mg/Lvitamin K₁ (#L10575, Alfa Aesar, Haverhill, Mass., USA), 5 mg/L hematin(#H3281, Sigma-Aldrich St. Louis, Mo., USA), and 0.25 g/L L-cysteine(#168149, Sigma-Aldrich). The medium was allowed to equilibrate insidethe anaerobic chamber for at least 24 hours before use.

MPN assays were performed in clear, sterile, non-treated polystyrene384-well plates (Nunc 265202, Thermo Fisher Scientific, Waltham, Mass.,USA). Two series of eight consecutive 10-fold serial dilutions wereprepared from each sample in sterile autoclaved saline solution(equilibrated inside the anaerobic chamber for at least 24 h) on clearsterile non-treated polystyrene 96-well plates (Corning Costar 3370,Corning, N.Y., USA). We injected 10 μL of each serial dilution from eachseries into four (eight total per dilution) culture-medium replicates(wells) filled with 90 μL of the BHI-S broth medium.

Plates were sealed with a breathable membrane (Breath-Easy BEM-1,Diversified Biotech) and incubated for 5 d at 37.0° C. inside theanaerobic chamber. The plates were lidless for the first 24 h tofacilitate uniform gas equilibration, then from 24 h to the end of theincubation period (120 h), a plastic lid was kept over the plates.

At the end of the incubation, the plates were scanned using a flatbedscanner (HP ScanJet 8250, Hewlett-Packard, Palo Alto, Calif., USA) inthe reflective mode with black background at 300 dpi resolution. Thepositive wells (replicates) were called by visually observing eachacquired high-resolution image. The MPN for each sample was calculatedusing Microsoft Excel with the “Calc_MPN” macro [125].

DNA was extracted from thawed GIT contents and mucosal sample aliquotspreserved in DRS solution with the ZymoBIOMICS DNA Miniprep Kit (D4300,Zymo Research) according to the manufacturer's instructions. Sampleswere homogenized on a bead-beater (MiniBeadBeater-16, Model 607, BioSpec Products, Bartlesville, Okla., USA) for 5 min at the default speedof 3450 RPM. Quantitative recovery of DNA across multiple orders ofmicrobial loads in the samples was previously verified in [1, 17].

DNA yield and purity in the extracts was evaluated via light absorbance(NanoDrop 2000c, Thermo Fisher Scientific) and via a fluorometric assay(Qubit dsDNA HS Assay Kit Q32854, Thermo Fisher Scientific) on afluorometer (Invitrogen Qubit 3, Thermo Fisher Scientific).

Quantitative PCR (qPCR) for 16S rRNA gene DNA copy enumeration was thenperformed. In particular, the qPCR reactions were set up in triplicatesfor each DNA sample. A single replicate reaction volume of 15 μLcontained 1.5 μL of the DNA extracts combined with the qPCR master mix(SsoFast EvaGreen Supermix, #172-5200, Bio-Rad Laboratories, Hercules,Calif., USA), forward and reverse primers (synthesized by Integrated DNATechnologies, San Diego, Calif., USA; FIG. 38) at a final concentrationof 500 nM, and ultrapure water (Invitrogen UltraPure DNase/RNase-FreeDistilled Water 10977-015, Thermo Fisher Scientific). Reactions were setup in white 96-well PCR plates (#HSP9655, Bio-Rad Laboratories) sealedwith a PCR tape (#MSB1001, Bio-Rad Laboratories).

The standard curve was built for each qPCR run based on the includedseries of 10-fold dilutions of the “standard” SPF mouse fecal DNAextract (with the quantified absolute concentration of 16S rRNA genecopies using digital PCR).

Amplification was performed with real-time fluorescence measurements(CFX96 Real-Time PCR Detection System, Bio-Rad Laboratories).Thermocycling conditions were used according to FIG. 39. The qPCR datafiles were analyzed using Bio-Rad CFX Manager 3.1 (#1845000, Bio-RadLaboratories) and the Cq data were exported to Microsoft Excel forfurther processing.

Digital PCR (dPCR) for absolute 16S rRNA gene DNA copy enumeration: wasalso performed. In particular, droplet digital PCR (ddPCR) reactionswere set up according to [1, 17]. Single replicate reaction volume of 20μL contained 2.0 μL of the DNA extracts combined with the ddPCR mastermix (QX200 ddPCR EvaGreen Supermix, #1864033, Bio-Rad Laboratories),forward and reverse primers (synthesized by Integrated DNA Technologies;FIG. 38) at final concentration of 500 nM each, and ultrapure water(Thermo Fisher Scientific).

Droplets were generated using DG8 cartriges (#1864008, Bio-RadLaboratories), droplet generation oil (#1864006, Bio-Rad Laboratories),and DG8 gaskets (#1863009, Bio-Rad Laboratories) on a QX200 dropletgenerator (#1864002, Bio-Rad Laboratories) and analyzed using a QX200Droplet Digital PCR System (#1864001, Bio-Rad Laboratories) usingdroplet reader oil (#1863004, Bio-Rad Laboratories). The ddPCR datafiles were analyzed using QuantaSoft Software (#1864011, Bio-RadLaboratories) and the raw data were exported to Microsoft Excel forfurther processing.

Thermocycling conditions were used according to [1, 17] and FIG. 40.Amplification was performed in PCR plates (#0030133374, Eppendorf,Hauppauge, N.Y., USA) sealed with pierceable heat seals (#1814040,Bio-Rad Laboratories) using PCR plate sealer (PX1, #1814000, Bio-RadLaboratories) on a 96-deep well thermocycler (C1000 Touch, #1841100),Bio-Rad Laboratories).

16S rRNA gene DNA amplicon barcoding for next generation sequencing(NGS) was then performed. In particular, the PCR reactions was set upaccording to [1, 17], in triplicates for each DNA sample.Single-replicate reaction volumes of 30 μL contained 3 μL of the DNAextracts combined with the PCR master mix (5PRIME HotMasterMix,#2200400, Quantabio, Beverly, Mass., USA), DNA intercalating dye(EvaGreen, #31000, Biotium, Fremont, Calif., USA) at the suggested bythe manufacturer concentration (×1), barcoded forward and reverseprimers (synthesized by Integrated DNA Technologies; FIG. 38) at finalconcentration of 500 nM each, and ultrapure water (Thermo FisherScientific). Reactions were set up in 0.2 mL white PCR tubes (#TLS0851)with flat optical caps (#TCS0803, Bio-Rad Laboratories). Thermocyclingconditions were used according to [1, 17] and FIG. 41. Amplification wasperformed with real-time fluorescence measurements (CFX96 Real-Time PCRDetection System, Bio-Rad Laboratories) and samples were amplified for avariable number of cycles until the mid-exponential (logarithmic) phaseto maximize the amplicon yield and minimize artifacts related toover-amplification [76].

A Digital PCR (dPCR) for Illumina library quantification was thenperformed. In particular, a single replicate reaction volume of 20 uLcontained 2.0 uL of the diluted amplicon sample ligated with theIllumina adapters, 10 uL of ddPCR master mix (QX200 ddPCR EvaGreenSupermix, #186-4033, Bio-Rad Laboratories), forward and reverse primers(synthesized by Integrated DNA Technologies; FIG. 38) targeting theIllumina P5 and P7 adapters respectively at the final concentration of125 nM each, and ultrapure water (Invitrogen). Thermocycling conditionswere used according to FIG. 42. PCR amplification and droplet analysiswere performed as above.

Barcoded sample quantification, pooling, library purification andquality control were then performed. In particular, triplicates of eachbarcoded amplicon sample were combined. Each samples wasdiluted×10⁵-10⁷-fold and the molar concentration of barcoded ampliconswas quantified using a home-brew ddPCR library quantification assay andKAPA SYBR FAST Universal qPCR Library Quantification Kit (#KK4824, KapaBiosystems, Wilmington, Mass., USA) according to the manufacturer'sinstructions (the qPCR reaction was set up same as above).

Barcoded samples were pooled in equimolar amounts. Pooled library waspurified using Agencourt AMPure XP beads (#A63880, Beckman Coulter,Brea, Calif., USA) according to the manufacturer's instructions andeluted with ultrapure water (Invitrogen).

The purified library was confirmed to have the 260 nm to 280 nm lightabsorbance ratio of >1.8 using a NanoDrop 2000c spectrophotometer(Thermo Fisher Scientific). The average amplicon size of approximately˜400 bp was confirmed with a High Sensitivity D1000 ScreenTape System(##5067-5584 and 5067-5585, Agilent Technologies, Santa Clara, Calif.,USA) using a 2200 TapeStation instrument (Agilent Technologies) and theAgilent 2200 TapeStation Software A02.01. (Agilent Technologies).

The molar concentration of the pooled library was measured using theddPCR and KAPA qPCR assays and the library was submitted for nextgeneration sequencing (NGS) with the sequencing primers described inFIG. 38.

Next generation sequencing was then performed. In particular, thelibrary was sequenced on a MiSeq instrument (Illumina, San Diego,Calif., USA) in a 300-base paired-end mode using a MiSeq Reagent Kit v3(#MS-102-3003, Illumina). PhiX control spike-in was added at 15%.

The same universal microbial 16S rRNA gene V4 primers (modified from[16, 18] and validated in [1, 17]) targeting the V4 region of the 16SrRNA gene from the 519 to 806 positions were used as PCR primeroligonucleotides for 16S rRNA gene DNA copy quantification andmultiplexed microbial community profiling based on 16S rRNA geneamplicon sequencing. Reverse barcoded primers for 16S rRNA gene DNAamplicon barcoding were according to [16]. (FIG. 38):

Primers targeting the P5 and P7 Illumina adapters for barcoded ampliconand pooled library quantification using the ddPCR assay were accordingto [16, 18-21].

Demultiplexed 2×300 reads were processed using the Qiime2-2019.01pipeline [80]. DADA2 plugin [3] was used to filter (forward trimming—5,forward truncation—230, reverse trimming—5, reverse truncation—160),denoise, merge the paired-end sequences, and remove the chimeras.Taxonomic sequence (amplicon sequence variant, ASV) classification wasperformed using the classifier (available for download from [126])trained [110] on the V4 515-806 bp regions of 16S rRNA gene sequencesfrom the Silva rRNA reference database, release 132 [9, 55,56](available for download from [127]).

Functional gene inference analysis with the PICRUSt2 [45, 46] wasperformed on the ASVs within the Qiime2 environment. Absolute andrelative abundances of ASVs were normalized using the inferred 16S rRNAgene DNA copy counts. Obtained predicted metagenome data were used tocalculate the normalized relative and absolute abundances of the geneorthologs of interest using Python tools (described below).

Sequencing Data handling, calculations, and statistical analyses wereperformed using Microsoft Excel with the Real Statistics Resource Pack[128], and the Python packages NumPy [129], Pandas [130], SciPy [131,132], Statsmodels [133]. Plotting was performed with Matplotlib

and Seaborn [135]. All Python packages were run using IPython [136]within Jupyter notebooks [137] distributed with the Anaconda environment[138].

Frequency data for the 16S rRNA gene ASVs assigned to taxa in eachsample were converted to relative abundances for each sample. Relativeabundances then were converted to absolute abundances using thecorresponding values of total 16S rRNA gene DNA loads obtained from theqPCR and ddPCR assays for each sample.

Absolute abundance data were then collapsed to the genus (FIG. 30, panelA) or order (FIG. 30, panels B,C) taxonomical levels using a custom madePython function (confirmed to yield identical results to the “collapse”method of the Qiime2 “Taxa” plugin [80]). The contaminating taxa weredefined (from sample handling during collection or from the DNAextraction kit or PCR reagents) using two methods: taxa that were notpresent in at least 1 out of 16 cecum contents samples (4 mice out of 6from each group×4 groups), and taxa identified with a frequency-basedcontaminant identification [139] implemented by us in Python. Data forchloroplasts and mitochondria of plant origin (likely from the chowdiet) were kept in the dataset for FIG. 30, panel A and panel C andremoved for FIG. 30, panel B. Mean absolute abundances of taxa for eachgroup were calculated, converted to relative abundances, and plotted inFIG. 30, panel B.

Principal components analysis (PCA) of the relative abundance data (FIG.36, panel B) was performed on centered log-ratio (CLR)-transformed [140,141] (after a pseudocount equal to the minimal non-zero sequence countin the dataset was added to all zero values) genus-level relativeabundance data using the Python Scikit-learn package [142].

PCA of the absolute abundance data (FIG. 30, panel A) was performed onlog₁₀-transformed and centered-standardized (converted tonormally-distributed data with mean=0 and standard deviation=1) [143]genus-level absolute abundance data using the Python Scikit-learnpackage [142].

Bile acid analysis was then performed. Reagents TαMCA, TβMCA, TωMCA,THCA, αMCA, βMCA, ωMCA, HCA, HDCA, MCA, GCDCA, GDCA, and GCA for bileacid analysis (FIG. 43) were obtained from Steraloids (Newport, R.I.,USA). The reagents TCA, CA, DCA, TCDCA, TDCA, TUDCA, TLCA, CDCA, UDCA,LCA, D₄-TCA, D₄-DCA, D₄-CA, D₄-TDCA, D₄-GLCA, D₄-GUDCA, D₄-GCDCA,D₄-GCA, and D₄-GDCA (FIG. 43) were obtained from Isosciences (Ambler,Pa., USA).

LC/MS grade acetonitrile (#A955-500), water (#W6500), and formic acid(#A117-50) were obtained from Thermo Fisher Scientific.

To overcome sample buffering (pH issues) in sample preparation, sampleswere extracted (using a protocol adapted and modified from [144-146]) in9 volumes of ethanol with 0.5% formic acid and nine different heavyisotope (D₄) internal standards at 5 μM. D₄ internal standards weretaurocholic acid (TCA), cholic acid (CA), deoxycholic acid (DCA),taurodeoxycholic acid (TDCA), glycocholic acid (GCA), glycolithocholicacid (GLCA), glycoursodeoxycholic acid (GUDCA), glycochenodeoxycholicacid (GCDCA), and glycodeoxycholic acid (GDCA). Samples were heated forone hour at 70° C. with orbital shaking at 900 RPM. Solids wereprecipitated by centrifugation at 17000 RCF for 15 minutes at 4° C.Supernatants were decanted as 10% of the original sample (e.g. 100 μL ofa 1 mL extraction sample) and evaporated at approximately 100 mTorr atRT on a rotovap (Centrivap Concentrator #7810016, Labconco, Kansas City,Mo., USA). The evaporated samples were reconstituted at 100× dilutionfrom the original sample (e.g. 100 μL decanted solution is resuspendedat 1 mL) in 20% acetonitrile, 80% water with 0.1% formic acid.

Due to small volumes, gall bladder bile samples were first diluted in 10volumes of 100% ethanol (#3916EA, Decon Labs, King of Prussia, Pa.,USA). The ethanol-based dilutions were combined with 9 volumes ofultrapure water (Invitrogen) and subjected to extraction as above.

Each 10 μL extracted and reconstituted sample injection was analyzed ona Waters Acquity UPLC coupled to a Xevo-qTOF Mass Spectrometer (Waters,Manchester, UK) using an Acquity UPLC HSS T3 1.8 micron, 2.1×100 mmcolumn (#186003539) and Acquity UPLC HSS T3 1.8 micron Guard Column(#186003976). Needle wash was two parts isopropanol, one part water, andone part acetonitrile. Purge solvent was 5% acetonitrile in water. Apooled quality control sample was run every 8 injections to correct fordrift in response.

Mass spectrometer instrument parameters were as follows: CapillaryVoltage 2.4 kV, Collision Energy 6.0 eV, Sampling Cone 90V, SourceOffset 40 V, Source 120° C., desolvation gas temperature 550° C., conegas 50 L/Hr, and desolvation Gas 900 L/Hr. Time-of-flight mass spectrawere collected in resolution mode, corresponding to 30000 m/Δm. The massaxis was calibrated with sodium formate clusters and locked usingleucine enkephalin.

A seven point external calibration curve was collected three timeswithin the run from 0.05 to 30 μM of the bile acid standards [0.05, 0.1,0.5, 1, 5, 10, 30 μM]. External standards were taurocholic acid (TCA),tauro-alpha-muricholic acid (TaMCA), tauro-beta-muricholic acid (TOMCA),tauro-omega-muricholic acid (ToMCA), tauro-hyocholic acid (THCA),tauro-deoxycholic acid (TDCA), tauro-ursodeoxycholic acid (TUDCA),tauro-chenodeoxycholic acid (TCDCA), taurolithocholic acid (TLCA),glyco-cholic acid (GCA), glyco-hyocholic acid (GHCA), glyco-deoxycholicacid (GDCA), glyco-hyodeoxycholic acid (GHDCA), cholic acid (CA),alpha-muricholic acid (aMCA), beta-muricholic acid (OMCA),omega-muricholic acid (oMCA), hyocholic acid (HCA, also known as7-muricholic acid), deoxycholic acid (DCA), chenodeoxycholic acid(CDCA), ursodeoxycholic acid (UDCA), hyodeoxycholic acid (HDCA),murocholic acid (murideoxycholic acid, MDCA), lithocholic acid (LCA),glycolithocholic acid (GLCA), glycourosodeoxycholic acid (GUDCA), andglycochenodeoxycholic acid (GCDCA). It was not possible to resolve UDCAand HDCA; so the sum was reported.

Integrated areas of extracted ion chromatograms were obtained usingQuanLynx (Waters, Milford, Mass., USA) and a mass extraction window of10 mDa. Final corrections accounting for drift in instrumentalsensitivity were performed in Microsoft Excel.

Elution Gradient: Samples were eluted using the following gradient ofwater with 0.1% formic acid (“A”) and balance of acetonitrile with 0.1%formic acid:

1. 0 min, 0.55 mL/min at 68% A

2. 2 min, 0.55 mL/min at 60% A, 10 curve

3. 5 min, 0.55 mL/min at 40% A, 5 curve

4. 6 min, 1.1 mL/min at 0% A, 10 curve

5. 6.2 min, 1.2 mL/min at 0% A, 6 curve

6. 6.5 min, 1.47 mL/min at 0% A, 6 curve

7. 8.9 min, 1.5 mL/min at 0% A, 6 curve

8. 9.0 min, 0.9 mL/min at 68% A, 6 curve

9. 10 min, 0.55 mL/min at 68% A, 6 curve

Bile acid data processing and analysis was performed using the softwaretools described for sequencing data processing.

Example 12: Absolute Quantification of Target Microbiota to VerifyEffect of Coprophagy in Mice

In this example, a pilot study was performed to confirm that preventingcoprophagy in mice would result in decreased viable microbial load andaltered microbiota composition in the small intestine.

A most probable number (MPN) assay utilizing anaerobic BHI-S brothmedium was used to evaluate the live (culturable) microbial loads alongthe entire GIT of mice known to be coprophagic (housed in standard cagesin groups, N=5) and mice known to be non-coprophagic (fitted with tailcups and housed in standard cages in groups, N=5).

Consistent with the published, classical literature [147, 148], it wasfound that coprophagic mice had significantly higher loads of culturablemicrobes in their upper GIT than mice that were non-coprophagic (FIG.36, panel A). Moreover, the microbial community composition in theproximal GIT, particularly in the stomach, of coprophagic mice moreclosely resembled the microbial composition of the large intestine (FIG.36, panel B) as revealed by 16S rRNA gene amplicon sequencing (N=1 mouseanalyzed from each group) and principal components analysis (PCA) of theresulting relative abundance data.

This pilot study confirmed that tail cups were effective at preventingthe self-reinoculation of viable fecal flora in the upper GIT of mice.These results spurred the Applicant to design a rigorous, detailed study(FIG. 28) to answer the three questions using the quantitative 16S rRNAgene amplicon sequencing (to account for both changes in the totalmicrobial load and the unculturable taxa), quantitative functional genecontent inference, and targeted bile-acid metabolomics analyses: (1) Doquantitative 16S rRNA gene amplicon sequencing tools detect differencesin small-intestine microbial loads between mice known to be coprophagicand non-coprophagic? (2) Does coprophagy impact the microbialcomposition of the small intestine? (3) Do differences in microbiotadensity and composition associated with self-reinoculation in miceimpact microbial function (e.g., alter microbial metabolite productionor modifications) in the small intestine?

Example 13: Absolute Quantification of Target Microbiota to VerifyEffect of Coprophagy in Mice-Study Design

A set of experiments was designed to provide a study directed todetermine the effect of coprophagy in mice by performing absolutequantification of target microbiota in mice.

The study design (FIG. 28) consisted of six cages of four animals eachthat were co-housed for 2-6 months and then split into four experimentalgroups and singly housed for 12-20 days.

The four experimental conditions were: animals fitted with functionaltail cups (TC-F) and singly housed in standard cages, animals fittedwith mock tail cups (TC-M) and singly housed in standard cages, animalssingly housed on wire floors (WF), and control animals singly housed instandard conditions (CTRL).

At the end of the study, gastrointestinal contents and mucosal sampleswere collected from all segments of the GIT of each animal and weevaluated total microbial loads (entire GIT) and microbiome composition(stomach (STM), jejunum (SI2), and cecum (CEC)).

The cecum segment of the large intestine was selected for quantitative16S rRNA gene amplicon sequencing because the analysis of the contentsof this section can provide a complete snapshot of the large-intestineand fecal microbial diversity in response to environmental factors[149-151]. Cecal contents also enabled us to collect a more consistentamount of sample from all animals across all experimental conditions(whereas defecation may be inconsistent among animals at the time ofterminal sampling).

Example 13: Absolute Quantification of Microbiota Indicates thatSelf-Reinoculation Increases Microbial Loads in the Upper Gut

The study set up according with the indications of Example 12 wasperformed to answer the a first question: can quantitative sequencingtools detect the difference in 16S rRNA gene DNA copy load in the upperGIT of mice known to be coprophagic and non-coprophagic?

With this goal in mind, total quantifiable microbial loads were analyzedacross the GIT using 16S rRNA gene DNA quantitative PCR (qPCR) anddigital PCR (dPCR). Preventing self-reinoculation in mice equipped withfunctional tail cups dramatically decreased the lumenal microbial loadsin the upper GIT but not in the lower GIT (FIG. 29, panel A). Totalquantifiable microbial loads in the upper GIT were reduced only in miceequipped with functional tail cups. All other experimental groups ofsingly-housed animals (those equipped with mock tail cups, housed onwire floors, or housed on standard woodchip bedding) that retainedaccess to fecal matter and practiced self-reinoculation had similarlyhigh microbial loads in the upper GIT, as expected from the publishedliterature [117, 152-157].

Across all test groups, mucosal microbial loads in the mid-smallintestine demonstrated high correlation (Pearson's R=0.84, P=2.8×10⁻⁷)with the microbial loads in the lumenal contents (FIG. 29, panel B).

Stomach (STM) and small-intestine (SI1, SI2, and SI3) samples from one(out of six) of the TC-F mice showed higher microbial loads comparedwith the other TC-F mice. The total microbial load in the upper GIT inthis TC-F mouse was similar to mice from all other groups (TC-M, WF,CTRL), which emphasizes the crucial importance of performing analyses ofboth microbial load and composition (discussed below) on the samesamples.

Example 14: Absolute Quantification Indicates that Self-ReinoculationSubstantially Alters the Microbiota Composition in the Upper Gut but hasLess Pronounced Effects in the Large Intestine

The study set up according with the indications of Example 12 wasperformed to answer the a second question: does self-reinoculation withfecal microbiota impact upper GIT microbial composition?

With this goal in mind, quantitative 16S rRNA gene amplicon sequencing[1, 17] was performed on stomach (STM), jejunum (SI2), and cecum (CEC)samples.

Qualitative sequencing revealed dramatic overall changes in the upperGIT microbiota caused by self-reinoculation (FIG. 30). An exploratoryPCA performed on the multidimensional absolute microbial abundanceprofiles highlights the unique and distinct composition of the upper GITmicrobiome of non-coprophagic mice (FIG. 30, panel A). It is noteworthythat the stomach (STM) and small-intestine (SI2) microbiota in allcoprophagic mice clustered closer to the large-intestine microbiota,suggesting the similarity was due to persistent self-reinoculation withthe large-intestine microbiota (FIG. 30, panel A).

Self-reinoculation had differential effects across microbial taxa (FIG.30, panel C), which could be classified into three main categoriesdepending on the pattern of their change:

-   -   1. “Fecal taxa” (e.g., Clostridiales, Bacteroidales,        Erysipelotrichales) that either dropped significantly or        disappeared (fell below the lower limit of detection [LLOD] of        the quantitative sequencing method [1, 17]) in the upper GIT of        non-coprophagic mice;    -   2. “True small-intestine taxa” (e.g., Lactobacillales) that        remained relatively stable in the upper GIT in non-coprophagic        mice;    -   3. Taxa that had lower absolute abundance in the cecum (e.g.,        Bacteroidales, Erysipelotrichales, Betaproteobacteriales) of        non-coprophagic (compared with coprophagic) mice.

The results demonstrated that preventing self-reinoculation dramaticallyreduced the total levels of several prominent taxonomical groups ofobligate anaerobes (e.g., Clostridiales, Bacteroidales,Erysipelotrichale) in the upper gastrointestinal microbiota ofconventional mice. Despite these differences in taxa, levels ofLactobacillales in the small intestine and cecum, but not in thestomach, remained similar between coprophagic and non-coprophagicanimals (FIG. 30, panel C). The physiological significance of themaintained persistent population of Lactobacillales in the uppergastrointestinal tract (e.g., stomach or small intestine) and theiroverall consistent presence along the entire GIT [158, 159] for the hostis not fully understood. However, Lactobacilli colonization in thestomach and small intestine has been shown to promote resistance tocolonization by pathogens (reviewed in [160, 161]).

Overall, the composition of the small-intestine microbiota ofcoprophagic mice was consistent with that previously reported inliterature [149]. The upper-GIT microbiota in non-coprophagic mice wasdominated by Lactobacilli (FIG. 30, panel C), known to be a prominentmicrobial taxon in human small-intestine microbiota [162-164].Importantly, the compositional analysis showed that the single TC-Fmouse that had high microbial loads in its stomach and small intestinehad a microbial composition in those segments of the GIT similar (i.e.,dominated by Lactobacillales) to all other TC-F mice, and very distinctfrom all coprophagic mice (FIG. 30, panels B,C). The PCA showed that thestomach and mid-small intestine of this mouse clustered with the stomachand mid-small intestine of all other TC-F mice (FIG. 30, panel A).

Example 15: Absolute Quantification of Target Microbiota Indicates thatChanges in the Small-Intestine Microbiota Lead to Differences inInferred Microbial Functional Gene Content

Absolute quantification was performed to verify whether the quantitativeand qualitative changes in the small-intestine microbiota induced byself-reinoculation result in altered microbial function [7, 165] and analtered metabolite profile, either indirectly, as a result of functionalchanges in the microbiota, or directly via re-ingestion of fecalmetabolites.

To understand how such alterations to microbiota would impact microbialfunction in the small intestine, experiments were carried out to predicthow the absolute abundances of functional microbial genes would beaffected. The pipeline for microbial functional inference based on the16S rRNA marker gene sequences (PICRUSt2) [45, 46] was coupled with thequantitative 16S rRNA gene amplicon sequencing approach [1, 17]. Theanalysis was focused on microbial functions that would be highlyrelevant to small-intestine physiology: microbial conversion ofhost-derived bile acids and microbial modification of xenobiotics.

It was found that the inferred absolute abundances of a number ofmicrobial gene orthologs implicated in enzymatic hydrolysis ofconjugated bile acids (bile salt hydrolase, BSH [166-168]) andxenobiotic conjugates (e.g., beta-glucuronidase, arylsulfatase [169,170]) in the stomach and the small intestine of coprophagic mice weredramatically higher (in some cases by several orders of magnitude) thanin non-coprophagic mice (FIG. 31). This difference was not observed inthe cecum.

Example 16: Absolute Quantification Indicates that Changes in theSmall-Intestine Microbiota Induced by Self-Reinoculation Alter the BileAcid Profile

Bile acids are a prominent class of host-derived compounds with multipleimportant physiological functions and effects on the host and its gutmicrobiota [171, 172]. These host-derived molecules are highly amenableto microbial modification in both the small and large intestine [173].The main microbial bile-acid modifications in the GIT includedeconjugation, dehydrogenation, dehydroxylation, and epimerization[172].

Thus, in this example quantitative bile acid profiling along the entireGIT was performed to evaluate the effects of self-reinoculation on bileacid composition.

The small intestine is the segment of the GIT that harbors the highestlevels of bile acids (up to 10 mM) and where they function in lipidemulsification and absorption [174-176]. Given these high concentrationsof bile acid substrates, we specifically wished to analyze whether thedifferences observed in small-intestine microbiota (FIGS. 29-30) betweencoprophagic and non-coprophagic mice would result in pronounced effectson microbial deconjugation of bile acids. Applicant also wished to testwhether any differences in bile-acid deconjugation were in agreementwith the differences in the absolute BSH gene content we inferred (FIG.31, panel A) from the absolute microbial abundances (FIG. 30, panel C).

It was first confirmed that in all four experimental groups, total bileacids levels (conjugated and unconjugated; primary and secondary) acrossall sections of the GIT were highest in the small intestine (FIG. 32,panel A). The levels of conjugated and unconjugated (FIG. 32, panel B)as well as primary (host-synthesized) and secondary (microbe-modified)bile acids (FIG. 37) were then compared between coprophagic andnon-coprophagic mice.

Across all sections of the GIT and in bile, non-coprophagic mice (TC-F)had significantly lower levels of unconjugated bile acids compared withcoprophagic mice (FIG. 32, panel B). Consistent with the computationalinference in FIG. 31, panel A (performed on mid-SI samples only), in allthree sections of the small intestine of non-coprophagic mice (TC-F),the levels of unconjugated bile acids were substantially lower than incoprophagic mice. Almost 100% of the total bile acid pool remained in aconjugated form in the small intestine of non-coprophagic mice.

In all groups of coprophagic mice (TC-M, WF, and CTRL) the fraction ofunconjugated bile acids gradually increased from the proximal to distalend of the small intestine. Gallbladder bile-acid profiling (FIG. 32,panel B) confirmed that bile acids were secreted into the duodenumpredominantly in the conjugated form in all coprophagic mice. Thispattern is consistent with the hypothesis that the exposure of bileacids to microbial deconjugation activity increases as they transit downa small intestine with high microbial loads (FIG. 29, panel A) [174].

In the large intestine, non-coprophagic (TC-F) mice carried a smallerfraction of unconjugated bile acids compared with all coprophagicexperimental groups (FIG. 32, panel B).

Bile acid deconjugation in the small intestine of coprophagic mice wasuniform for all glyco- and tauro-conjugates of all primary and secondarybile acids measured in our study, suggesting a broad-specificity BSHactivity was provided by a complex fecal flora in the small intestine ofthose animals.

In gallbladder bile and across all segments of the GIT from the stomachto the cecum, non-coprophagic mice had a statistically significantlylower fraction of total secondary bile acids (conjugated andunconjugated) than coprophagic mice (FIG. 37). This change was uniformfor the entire secondary bile acid pool of those analyzed. The onlysegment of the gut in which the difference in the fraction of secondarybile acids was not statistically significant between coprophagic andnon-coprophagic mice was the colon. In fact, the differences in thefractions of total unconjugated and total secondary bile-acids betweencoprophagic and non-coprophagic mice would have gone largely undetectedhad we only analyzed colonic contents or stool. These findings furtherhighlight the importance of the comprehensive spatial interrogation ofthe complex crosstalk between the microbiota and bile acids in thegastrointestinal tract.

Example 17: Absolute Quantification Performed on 16 rRNARibonucleotides—Prophetic

Samples can be collected from mice and the related RNA extracted.

The total RNA can be extracted from fresh or frozen fecal samples usingQiagen Allprep Powerviral DNA/RNA kit (28000-50; Qiagen) according tothe manufacturers protocol.

RNA can then be reverse transcribed into DNA. Briefly, a 20 uL reactionwas set up including 10 uL of sample, 0.25 uL of WarmStart RTx (M0380L;New England Biolabs Inc), 9.25 uL of nuclease free water, 0.5 uL ofRiboguard RNase inhibitor (RG90910K; Lucigen), 1.25 uL of 10 mM dNTP mix(N0447S; New England Biolabs Inc), 2.5 uL of 10× isothermalamplification buffer (B0537S; New England Biolabs Inc), and 1.25 uL ofreverse (UN00R0, ‘-GGACTACHVGGGTWTCTAAT-3’ [16, 18]) primer (SEQ ID NO:26) (Integrated DNA Technologies, San Diego, Calif., USA) at the finalconcentration of 500 nM.

The reverse transcription thermocycling protocol can be set up asfollows: Primer annealing at 25° C. for 5 mins, cDNA synthesis at 55° C.for 10 mins, and enzyme heat inactivation at 80° C. for 10 mins.

Assay can be performed on a real-time PCR instrument (CFX96 Real-TimePCR Detection System, Bio-Rad Laboratories).

The output cDNA template from this step then followed the protocol inExample 1 for generation of barcoded amplicon libraries for sequencing.

Example 18: Tail Cup Design to Prevent Self-Reinoculation

Functional tail cups have been shown to reliably prevent theself-reinoculation with fecal flora. However, the tail cup approach haslimitations. Tail cups in the current design may not be suitable forfemale rodents due to anatomical differences leading to urine enteringand remaining inside the devices [177]. Animals need to be singly housedto prevent them from gnawing on each other's tail cups and causingdevice failure or injury. The tail cup approach may be hard to implementin younger and actively growing mice (e.g., before or around weaning).Some mice in the study developed self-inflicted skin lesions fromover-grooming at the location where the tail cups come in contact withthe body at the animal's hind end. Thus, the approach in its currentimplementation is limited to 2-3 weeks in adult animals.

Applicant's device design reduced the risk of tail injury and necrosisdescribed in previous works [178] and allows for emptying the cups onlyonce every 24 hours to reduce handling stress. Because host stress canaffect the microbiota [179] and other physiological parameters, a mocktail-cup group was included. Both TC-F and TC-M mice demonstrated asimilar degree of weight loss (FIG. 35, panel A) when compared with theWF and CTRL mice despite similar food intake rates across all fourgroups (FIG. 35, panel B). Mice fitted with mock tail cups (TC-M) hadmicrobial patterns and bile acids profiles similar to control mice(CTRL), thus the effects we observed in non-coprophagic mice are notattributable to stress.

The current tail cup approach is also implementable in gnotobioticsettings (e.g., flexible film isolators and individually ventilatedcages), which can aid studies that involve association of mice withdefined microbial communities or with human-derived microbiota.

An additional description is provided with reference to FIGS. 45-48showing an exemplary a tail cup device (100) for animals such asrodents, the device comprised of a cup (110) for trapping excreted fecesand a tail sleeve (120) for mounting of the cup (110) at a tail base ofa rodent having a sufficiently long tail, such as mice (including deermouse, Natal multimammate mouse, vesper mouse, long-tailed pocket mouse,little pocket mouse, canyon mouse, members of the genus harvest mouse)or rats (including cotton rat, obese sand rat, rice rat, white-tailedrat, kangaroo rat, desert woodrat), degu, voles (bank, red-backed vole,meadow vole, mountain vole, tundra vole, prairie vole, woodland/pinevole, Brandt's vole, California vole), gophers (e.g., pocket gophers),mole-rats (e.g., naked mole-rat, Damaraland mole-rat), and moles, thetail sleeve (120) being configured to be applied to the tail of therodent.

In the illustration of FIGS. 45-48, the cup (110) is a tubular-shapedcomponent configured to trap fecal matter and prevent the rodent fromaccessing it, with an exemplary length of 2-5 cm and an exemplarydiameter of 1-3 cm. While FIGS. 45-48, show a circular or oval crosssection by way of example, any other shape, e.g. rectangular, can bedevised by the person skilled in the art. Additionally, while thedrawings show a generally uniform cross-section of the cup along itslength, embodiments are also possible where larger cross-section areasare provided close to one end of the cup (110) and smaller cross-sectionareas are provided close to the other end of the cup (110).

In the illustration of FIGS. 45-48, the distal end or surface (130) ofthe tubular cup (110) comprises an orifice (140) operating as a lockingopening of the cup (110) to allow passing through of the tail sleeve(120) from the inside to the outside of the cup (110). The orifice (140)may be round, oval or of similar shape with an exemplary 0.5-0.7 cmdiameter. In order to allow a proper locking engagement of the tailsleeve (120) when applied to the tail of the rodent and when pressure isnot applied to the sides of the cup (110), the diameter (or at least oneof the two axes) of the orifice (140) is smaller than the diameter ofthe tail sleeve (120). While the figures show a central placement of theorifice (140) on the distal surface (130), off-center placements arealso possible. Off-center placement of the orifice, for example closerto the dorsal side of the cup, would allow for an increased size(centrally asymmetric on the cross-section and linearly asymmetric on alongitudinal section) cup compartment on the ventral side of the cupwhere fecal matter may accumulate under gravity when animals spend mostof their time in their natural prone position.

In the illustration of FIGS. 45-48, distal surface (130) also includesan unlocking slit (150A-D) for opening of the cup (110) before or afteruse. Unlocking slit (150A-D) has a narrow diameter when compared withother dimensions of the cup (110), usually less than 1.0 mm. While theunlocking slit includes portions (150A), (150B) across the orifice (140)on the distal surface (130), intersecting the orifice (140) and spanningalong a diametral extension of the distal surface (130), it alsoincludes side portions (150C), (150D) extending along opposite sidewalls of the cup (110). Other embodiments can also be provided (e.g. incase the locking orifice is placed off-center) where the unlocking slitcrosses the orifice along a chord extension (for circular devices) ofthe distal surface. The purpose of the unlocking slit is toinstall/unlock the tail sleeve (120) in/from the orifice (140) byincreasing the gap formed by the orifice (140) through pression (e.g.with fingers, such as thumb on one side and index on the other whileholding the cup) alongside portions (150C), (150D), e.g. on pressingpoints (150CC) and (150DD), corresponding to the ends of theirrespective side portions. These pressing points can have no specificshape at all and just be located at the straight end of their respectiveside portions, or can have a shape (e.g. circular with a 1-2 mmdiameter) to address deformation stress dissipation concerns uponenlargement of the orifice. If desired, as also shown in the drawings,the pressing points (150CC), (150DD), can be placed on opposite sides ofthe cup (110) (e.g. 180 degrees apart in case of a cylindricalembodiment) to allow for a stronger hold of the cup (110) while applyingpressure, thus providing better structural integrity and responsivenessto the pressing force. The length of side portions (150), (150D) dependson parameters such as cup length, shape, cross section profile, size andmaterial and should be chosen to allow a sufficient increase of theorifice and unlocking slit when unlocking the cup (110) from the tailsleeve (120) upon application on the pressing points (150CC), (150DD) toallow removal of the cup (110) from the rodent and/or related emptyingof the cup (110), while not compromising the mechanical integrity of thedevice, not increasing the risk of the locking mechanism failure, andthe cup's purpose to effectively entrap fecal matter. While the figuresexemplary show a flat arrangement of the distal surface (130), suchsurface can also be spherical, conical or differently shaped, ifrequired.

In the illustration of FIGS. 45-48, reference will now be made to theproximal end or surface (160) of the cup (110), the proximal end havinga cross sectional dimension sufficiently wide to fit around a posteriorend of the rodent more proximal than the anus to ensure falling of thefecal pellets into the cup, but also preferably more distal than theurethral opening and genitals to prevent urine from accessing the cupand from discomfort or damage to the genital area of the animal. Whilethe proximal surface (160) can be shaped as a straight/flat cut as shownin the figures, embodiments are also possible where the proximal surfaceis carved or shaped to better fit around the rodent's posterior end andbetter accommodate for the genital anatomy of the rodent, varyingbetween genders.

In the illustration of FIGS. 45-48, a reinforcement and/or protectivering (170) is located along the proximal end (160) and is configured tocome in contact with a body (skin) portion close to and/or around thegenital area of the rodent, which portion the rodent may be able toreach with its mouth and/or teeth. The reinforcement and/or protectivering (170) is made from an inert (in order not to corrode or leak anychemical compounds) material hard enough to prevent the animal fromdamaging it by chewing (which would necessitate cup replacement), suchas metal (e.g. medical grade stainless steel, titanium and/or suitablemetal alloys), ceramic, glass, tough plastic (such as PTFE/Teflon orKevlar) and/or combinations of the same. Usage of soft materials wouldlikely result in deterioration of the proximal end of the cup due torodents chewing on it, thus resulting in the proximal edge of the cupbecoming jagged or sharp and potentially leading to severe skin damagewhen the rodent moves around and the cup's edge rubs against the skin.

In the illustration of FIGS. 45-48, the reinforcement and/or protectivering (170) comprises a proximal flange, an internal conical (funnel) orround section and a distal cylindrical part.

The internal section of the reinforcement and/or protective ring (170)fitting around the animal body may have a conical shape to allow for amore effective fecal entrapment inside the cup in cases where the rodentis allowed to freely mode around, frequently resulting in the tail cupand the animal's tail tilting to the sides away from the longitudinalbody axis. On the other hand, a round shape of the internal section hasthe advantage of serving as a joint surface when the animal moves aroundand the cup rubs against the animal's body/skin. Overall the design ofthe proximal end of the cup should allow for some degree offreedom/motion (not only axial rotation) relatively to the animal'sposterior end, at least partially matching the degree of freedom/motioncharacteristic for the tail base, in order to minimize or eliminate anyinhibition of animal's physical activity/motion.

On the other hand, in the illustration of FIGS. 45-48, the outsidediameter of the flange of the reinforcement and/or protective ring (170)is comparable to or larger than the cross-sectional extension (e.g.diameter) of the cup to ensure that the edge of the proximal surface(end) (160) of the cup (110) is not exposed to chewing. To furtherprevent animals from chewing on the proximal edge of the cup, thereinforcement and/or protective ring (170) may be installed to allow forsome gap (e.g. 2-3 mm) between the flared edge of the ring and the edgeof the proximal surface (edge) of the cup. If desired, the flared edgeof the ring (170) may also be configured to wrap around the edge of thecup. Given that the material of the ring is harder than the material ofthe cup this provides better protection from the animal's teeth.

Additionally, placement of the reinforcement and/or protective ring(170) at the proximal end of the cup (110) may be adjustable in order tocontrol the snug fit of the cup (110) against the animal's posterior endafter installation of the tail sleeve (120).

In its current exemplary implementation, reinforcement and/or protectivering (170) is made by a stainless steel grommet with reduced flange edgediameter and length, performed with a cutter on a lathe to improve sizeand reduce weight, thus resulting in a straight proximal edge of the cup(110).The person skilled in the art will understand that if the proximaledge is carved more anatomically than the reinforcement and/orprotective ring, it will have to be shaped accordingly.

As shown in the illustration of FIGS. 45-48, reinforcement and/orprotective ring (170) is preferably coupled (e.g. attached) to cup (110)using a coupling ring (175) (made of e.g. latex or plastic tubing).Presence of the coupling ring (175) also allows adjusting the depth andplacement of the reinforcement and/or protective ring (170) inside thecup (110), thus tuning the fit or snugness of the cup (110). Embodimentsare also possible where coupling ring (175) is not needed when the cupitself is made such that the reinforcement and/or protective ring (170)can be attached to the cup directly. It should be noted that a simplesoldering and/or gluing of the ring would not be preferred, as it wouldnot allow an adjustable arrangement.

Cup (110) may be made from a clear (e.g. transparent, such aspolypropylene) material to allow for an easier observation of the devicedegree of filling with animal excretions. However, an opaque (e.g.non-transparent) material may be preferred in cases where the excretionsneed to be protected from light, e.g. for further analysis. The materialcan be, for example, a mesh material with an exemplary mesh size of upto 1 mm.

Cup (110) may also comprise venting perforations or boreholes (180) toallow for the fecal excretions to accelerate the desiccation of trappedfecal excretions and prevent moisture entrapment. Advantageously, dryingfecal pellets distribute uniformly within the cup when the animal movesaround. If fecal excretions are not allowed to dry, they couldpotentially stick to the inside surface of the cup and build up aroundthe anus, likely imposing some resistance for further defecation, animportant animal welfare consideration. The venting perforations (180)can be of different shape, size, number, and distribution consideringthat: a) their size should be small enough and their shape (e.g. round)should be designed to prevent the fecal pellets from falling out,especially after drying and/or shrinking. The number of the perforationsshould be sufficiently large and their distribution should besufficiently uniform to allow faster fecal matter desiccation.

Reference will now be made to the tail sleeve (120) of the illustrationof FIGS. 45-48, which is configured to hold the cup snugly against theposterior end of the animal while at the same time maximizingdistributing the opposing force over a larger surface area of the tailskin to reduce the damaging effects of such shear force on the skin andother potential negative effects such as tail strangulation. At the sametime the sleeve (if made from a stiffer material) should only cover afraction of the tail (e.g. less than a half of the total length) toallow for some degree of freedom/motion of the distal part of the tailand not to inhibit the animal's movement.

As shown in FIG. 47, tail sleeve (120) comprises a longitudinally splitor open tubular component (210) (having an exemplary length of 2-5 cm,an exemplary inside diameter of ⅛″-comparable to or slightly smallerthan the tail outside diameter at the tail base- and an exemplaryoutside diameter of ¼″) and an intermediate locking groove (220) on itsoutside surface, the latter configured to allow locking of the tailsleeve (120) through the orifice (140) of the cup (110). In particular,the outside diameter of the groove (220) can be smaller than the outsidediameter of the tubular component (210) and also slightly smaller thanthe orifice or locking opening (140) of the cup (110). In order toaccommodate for variable tail diameters among animals along the taillength, a longitudinal strip of the wall of the sleeve (e.g. 1-2 mmwide) can be optionally removed to prevent uneven tail compression andto facilitate uniform application of adhesive force as later explained.

In the illustration of FIGS. 45-48, the tubular component can becylindrically or conically shaped (to accommodate the slightly conicaltail shape) and may have various degrees of softness or stiffness, butit should be sufficiently soft to conform to the tail shape uponinstallation without applying excessive pressure and sufficiently stiffnot to overstretch or deform in order to withstand the shear force fromthe snugly fit cup (110) when locked onto it. As with the cup (110),also the sleeve (120) may be opaque or clear for easier tail healthmonitoring. In accordance with an embodiment of the disclosure, thetubular component is made from a material devoid of components that uponleaking from the material can be toxic to the animal (e.g.,plasticizer-free tubing) as it can come in contact, in some cases, withthe tail skin through a layer of curable adhesive or adhesive tape, bothof which can potentially aid the extraction of toxic components from thematerial of the tubular component. Potential alternatives can includesurface patterning, e.g. nano- or micro-perforations to providegecko-like adhesion. More generally, any other means that allows theinside surface of the sleeve to be sufficiently adhesive and/or stickycan be devised by the person skilled in the art.

In the illustration of FIGS. 45-48, while the figures show a tail sleevemade of a tubular component cut along its entire length, otherrealizations are possible where the cut partially occurs only for a setlength starting at the proximal end, in order to accommodate the portionof the tail immediately following the tail base, i.e. the part of thetail that is largest in diameter.

The intermediate locking groove (220) extends perpendicularly to thelongitudinal direction of the sleeve (120) along the outside surface ofthe sleeve (120). As already noted above, the groove allows locking ofthe tail sleeve (120) through the orifice (140) of the cup (110). Ifdesired, multiple such locking grooves (220) can be provided in aparallel arrangement along the longitudinal extension of the tail sleeve(120) in order to provide for adjustable locking degrees and extensionsof the sleeve (120) on the cup (110) thus allowing an easy adjustment ofthe snugness of the cup fit once the sleeve is installed on the animal'stail.

Alternatively to the one or more locking grooves (220), the tail sleevecan have a tubular component with a variable outside diameter along itslength, where the proximal (relatively to the desired lockingpoint/level) portion of the tail sleeve has an outside diameter slightlysmaller than the locking opening of the cup, and the distal (relativelyto the desired locking point/level) portion of the sleeve has an outsidediameter larger than the locking opening of the cup.

Additionally, if desired, the distal edge of the tail sleeve may betapered along its inside diameter to prevent distal tail skin (at thedistal edge of the tail sleeve) from bulging up due to the applied shearforce (directed distally) from snug cup fitting and strangulating thedistal end of the tail.

If necessary and/or required, the tail sleeve (120) can be secured tothe tail skin surface of the animal by an adhesive, such as curableadhesive, curable glue, double-sided adhesive tape, the alternativeadhesion means described above. Use of double-sided adhesive tape(opaque or clear for easier tail health monitoring) appears to bepreferable as it allows instantaneous tail sleeve installation. Inparticular, the tape can be pre-applied to the inside surface of thetail sleeve which can be then be almost instantaneously placed onto theanimal's tail while the animal is restrained for a very short amount oftime (about 5-15 seconds). Additionally, with the double-sided adhesivethe tail sleeve and the tail cup can be easily removed and placed backat desired times without causing any skin surface damage.

Advantageously, when mounted, the tail cup (110) may freely rotate alongits longitudinal axis in order to ensure that the edges of the lockingopening (140) do not press too hard on the tail sleeve (120), do notstrangulate the animal's tail and at the same time are not under anexcessive shear force or stress due to the snugness of the cup fit.

According to several embodiments, the above described device preventscoprophagy in rodents for a large amount of experimentations, it allowsfecal collection in rodents for downstream fecal analyses (no need foranimals to be housed on wire floors or metabolic cages) and it allowsseparation of fecal excretions from urine excretions when either ofthese need to be collected without cross-contaminating each other withtheir corresponding components/analytes.

In summary, the description of the present example shows a novelcombination of tail cup and tail sleeve together with methods forinstalling them on an animal such as a rodent, by preventing coprophagyand making the gut microbiome of the rodent more human-like, thusallowing obtainment of a rodent model of the human digestive tract, inparticular small intestine.

Example 19: Rodent Model with a “Humanized” Digestive Tract

The upper gastrointestinal tract plays a prominent role in humanphysiology as the primary site for enzymatic digestion and nutrientabsorption, immune sampling, and drug uptake. Alterations to thesmall-intestine microbiome have been implicated in various humandiseases, such as non-alcoholic steatohepatitis and inflammatory bowelconditions. Yet, the physiological and functional roles of thesmall-intestine microbiota in humans remain poorly characterized becauseof the complexities associated with its sampling. Rodent models are usedextensively in microbiome research and enable the spatial, temporal,compositional, and functional interrogation of the gastrointestinalmicrobiota and its effects on the host physiology and disease phenotype.Classical, culture-based studies have documented that fecal microbialself-reinoculation (via coprophagy) affects the composition andabundance of microbes in the murine proximal gastrointestinal tract.

This pervasive self-reinoculation behavior could be a particularlyrelevant study factor when investigating small-intestine microbiota.Modern microbiome studies either do not take self-reinoculation intoaccount, or assume that approaches such as single housing mice orhousing on wire mesh floors eliminate it. These assumptions have notbeen rigorously tested with modern tools. Here, we used quantitative 16SrRNA gene amplicon sequencing, quantitative microbial functional genecontent inference, and metabolomic analyses of bile acids to evaluatethe effects of self-reinoculation on microbial loads, composition, andfunction in the murine upper gastrointestinal tract.

In coprophagic mice, continuous self-exposure to the fecal flora hadsubstantial quantitative and qualitative effects on the uppergastrointestinal microbiome. These differences in microbial abundanceand community composition were associated with an altered profile of thesmall-intestine bile acid pool, and, importantly, could not be inferredfrom analyzing large-intestine or stool samples. Overall, the patternsobserved in the small intestine of non-coprophagic mice (reduced totalmicrobial load, low abundance of anaerobic microbiota, and bile acidspredominantly in the conjugated form) resemble those typically seen inthe human smallintestine.

A combined use of the tail-cup herein described and absolutequantification method herein described, resulted in development of arodent model of the human digestive tract.

Human microbiota-associated (HMA) mice are broadly used in biomedicalresearch to investigate the causal roles of human microbiomes on thehost physiology and disease predisposition andphenotype. According therodent model herein described can be used in several applications, suchas biomedical, pharmaceutical research, and personalized medicine aswell as basic research biology and additional applications identifiableby a skilled person.

The non-coprophagic rodent model herein described is more similar tohumans both in terms of the microbial loads in the uppergastrointestinal tract (e.g., small intestine) and the microbiotacomposition (e.g., dominated by Lactobacilli spp.).

In particular, non-coprophagic mice have been provided that have asignificantly altered small intestinal bile acid profile and areexpected to have other physiological effects (e.g., small-intestinemucosal enzymatic activity and immunity, nutrient and xenobioticsuptake, and others) associated with the more human-like patter ofgastrointestinal microbial colonization.

It is expected that non-coprophagic mice herein described can becolonized with patient-derived microbiota as a more human-likehuman-microbiota-associated to provide a “patient-derived microbiomexenograft” model for various applications as will be understood by askilled person, such as personalized medicine to investigate in vivo thefunction of the patient's microbiome, its response to and modificationby xenobiotics, screening of compounds for its selective modification,and additional applications identifiable by a skilled person.

Accordingly, a non-coprophagic rodent model is described and related usein methods and systems (e.g. kit of parts) for use in study andinvestigations of the human digestive tract and in particular of thesmall intestine.

Exemplary applications where the non-coprophagic rodent model of thedisclosure can be used comprise applications using:

-   -   conventional specific-pathogen-free (SPF) mice housed in        standard animal laboratory conditions;    -   gnotobiotic or germ-free mice housed in specialized conditions        such as gnotobiotic chambers or cages designed to maintain their        gnotobiotic or germ-free status;    -   metabolic cages or any kind of specialized setup designed to        monitor animas in real time, monitor behavior, movement, food        and fluid consumption, collect urine samples, and or collect        fecal samples (e.g., “metabolic cage”), especially when urine        and feces have to be collected without cross-contaminating each        other with the analytes contained in them; when fecal output or        gastrointestinal transit needs to be monitored, and/or    -   combination of the above.

Several applications where the non-coprophagic rodent model of thedisclosure can be used can be identified in fields such as, probioticresearch, toxicology, pharmacology, pharmacokinetics, and additionalfields identifiable by a skilled person.

Non-coprophagic human like rodent models can also be further customizedby modification of the rodent gut microbiome as described herein toprovide non-coprophagic rodent models having target microbiomecharacterized by a microbiome profile formed by presence, proportion andtotal load of target prokaryotes. Exemplary target microbiome can beidentified in the following Examples 20-25 related to microbiome of thedigestive tract of human beings identified in patients who areundergoing either an oral double balloon endoscopy procedure or anesophagogastroduodenoscopy procedure as part of their clinical care.

Example 20: General Protocols for Profiling Human Small-IntestineMicrobiome

The following experimental protocols were carried out to assess theabsolute microbial loads in the human duodenum and their potentialrelationship with factors related to health and disease. The recentlydeveloped digital PCR anchored 16S rRNA gene amplicon sequencing method,also referred to as “quantitative sequencing”, was used to provideabsolute total microbes, absolute taxon abundances and filter outcontaminants in samples with low microbial abundance. By capturing theabsolute microbial abundances of the human duodenal and oral microbiome,one can better understand the makeup of the human duodenal microbiome,improve the understanding of the underlying community structure ofcertain diseases such as small intestinal bacterial overgrowth (SIBO)and determine how microbial load and composition correlate with upper GIsymptoms.

Study population and design: The REIMAGINE (Revealing the EntireIntestinal Microbiota and its Associations with the Genetic,Immunologic, and Neuroendocrine Ecosystem) study was conceived toexplore the relationships between the small-intestine microbialpopulations and different conditions and diseases[105]. Male and femalesubjects aged 18-80 years undergoing standard-of-care upper endoscopy(esophagogastroduodenoscopy, EGD) without colon preparation wereprospectively recruited. The study protocol was approved by theInstitutional Review Board (IRB) at Cedars-Sinai Medical Center, andsubjects provided informed written consent prior to participation (IRBProtocol: 00035192). Data presented here represents a retrospectiveanalysis of this prospectively collected information.

Questionnaires: Prior to EGD, all subjects completed a studyquestionnaire documenting demographic information and family and medicalhistory, including GI disease and bowel symptoms, medication use, use ofalcohol and recreational drugs, travel history, and dietary habits andchanges. Subjects also reported any known underlying conditions, such asGI diseases and disorders, neurologic disease, hematologic disease,autoimmune disease, kidney disease, heart disease and cancer. Allmedical information provided by subjects was verified through audits ofmedical records. All data were de-identified prior to analysis.

Blood collection and analysis: After completing the study questionnaire,fasting blood samples were collected in BD Vacutainer SST tubes (BectonDickson, Franklin Lakes, N.J., USA). Levels of circulating pro- andanti-inflammatory cytokines and chemokines were analyzed on a LuminexFlexMap 3D (Luminex Corp., Austin, Tex., USA) using a bead-basedmultiplex panel that included: GM-CSF, IFNγ, IL10, IL12P70, IL13, IL1B,IL2, IL4, IL5, IL6, IL8, MCP1 and TNFα (EMD Millipore Corp., Billerica,Mass., USA, cat. #HCYTOMAG-60K).

Saliva and small-intestine luminal sample collection: Prior to EGDprocedure, saliva was collected in a sterile 5 mL tube. During the EGDprocedure, samples of duodenal luminal fluid were procured using acustom-designed sterile aspiration double-lumen catheter (Hobbs Medical,Inc.)[180]. Duodenal aspirates (DA) were collected using acustom-designed sterile inner catheter which was pushed through asterile bone wax cap only after the endoscopist entered the secondportion of the duodenum, in order to reduce contamination from themouth, esophagus, and stomach. After collection, samples wereimmediately placed on ice and transferred to the laboratory for furtheranalysis.

Aspirate processing and microbial culture: Prior to microbial culture,an equal volume of sterile 1× dithiothreitol (DTT) prepared with RNaseand DNase PCR-grade sterile water was added to each saliva and duodenalaspirate (˜1 mL) and the samples were vortexed until fully liquified(˜30 sec) as described previously[180]. A 100 μl aliquot of eachduodenal sample (DA+DTT) was then serially diluted with 900 μL sterile1× PBS and plated on MacConkey agar (Becton Dickinson), and on bloodagar (Becton Dickinson). Plates were incubated at 37° C. for 16-18 hunder aerobic (MacConkey) or anaerobic (blood agar) conditions. Plateswithout bacterial growth after 18 h were re-incubated for an additional18 hours. Colony forming units (CFU) were then counted electronicallyusing a Scan 500 (Interscience, Paris, France). Saliva+DTT and theremainder of each DA+DTT were centrifuged at maximum speed (>13000 RPM)for 5 min. The supernatant was removed, and 1 mL of sterile Allprotectreagent (Qiagen, Hilden, Germany) was added to the microbial pellet andthen stored at −80° C.

DNA isolation: On the day of the DNA isolation, DA pellets were thawedon ice and processed as described previously[180]. Microbial DNA wasisolated using the MagAttract PowerSoil DNA KF Kit (Qiagen) on aKingFisher Duo (Thermo Fisher Scientific, Waltham, Mass., USA), andquantified using Qubit dsDNA high sensitivity and Qubit dsDNA BR Assaykits (Invitrogen by Thermo Fisher Scientific) on a Qubit 4 Fluorometer(Invitrogen, Carlsbad, Calif., USA).

16S rRNA gene sequencing: Extracted DNA was amplified, barcoded andsequenced as described previously[2, 181, 182]. Briefly, amplificationof the variable 4 (V4) region of the 16S rRNA gene was performed in 20uL duplicate reactions with: 8 uL of 2.5×5Prime Hotstart Mastermix (VWR,Radnor, Pa., USA), 1 uL of 20× Evagreen (VWR), 5 uL each of 5 uM forwardand reverse primers (519F, barcoded 806R, IDT, CoralVille, Iowa, USA),3.5 uL of water, and 3.5 uL of extracted DNA template. A CFX96 RT-PCRmachine (Bio-Rad Laboratories, Hercules, Calif., USA) was used tomonitor amplification reactions and all samples were removed in lateexponential phase (˜10,000 FRU) to minimize chimera formation andnon-specific amplification[113, 114, 181]. Amplification was performedunder the following cycling conditions: 94° C. for 3 min, up to 50cycles of 94° C. for 45 s, 54° C. for 60 s, and 72° C. for 90 s. Severalsamples were rerun after diluting the template as they showednon-exponential amplification in the undiluted sample, a sign of PCRinhibition. Amplified duplicates were pooled together and quantifiedwith KAPA library quantification kit (Roche, Basel, Switzerland) andthen all samples were pooled at equimolar concentrations with up to 96samples per library. AMPureXP beads (Beckman Coulter, Brea, Calif., USA)were used to clean up and concentrate libraries before final libraryquantification with a High Sensitivity D1000 Tapestation Chip (Agilent,Santa Clara, Calif., USA). Illumina MiSeq sequencing was performed witha 2×300 bp reagent kit by Fulgent Genetics (Temple City, Calif., USA).

Raw reads were demultiplexed by Fulgent Genetics. Demultiplexed forwardand reverse reads were processed with QIIME 2 2020.2[183]. Loading ofsequence data was performed with the demux plugin followed by qualityfiltering and denoising with the dada2 plugin[184]. Dada2 trimmingparameters were set to the base pair where the average quality scoredropped below thirty. All samples were rarefied to the lowest read depthpresent in all samples (45,386 reads) to decrease biases from varyingsequencing depth between samples[109]. The q2-feature-classifier[185]was then used to assign taxonomy to amplicon sequence variants (ASV)with the Silva[56] 132 99% OTUs references. Resulting read count tableswere used for downstream analyses in IPython notebooks (see DataAvailability Section).

Absolute abundance: Briefly, the Bio-Rad QX200 droplet dPCR system(Bio-Rad Laboratories) was utilized to measure the 16S concentration ineach sample with the following reaction components: 1X QX200 EvaGreenSupermix (Bio-Rad), 500 nM forward primer, and 500 nM reverse primer(519F, 806R) and thermocycling conditions: 95° C. for 5 min, 40 cyclesof 95° C. for 30 s, 52° C. for 30 s, and 68° C. for 30 s, followed by adye stabilization step of 4° C. for 5 min and 90° C. for 5 min. Thefinal concentration of 16S rRNA gene copies in each sample was correctedfor dilutions and normalized to the extracted sample volume

For each sample, the input-volume-normalized total microbial load fromdPCR was multiplied by each amplicon sequence variant's (ASV) relativeabundance to determine the absolute abundance of each ASV. The averageof all sample volumes for a specific sample type was used for a fewsamples (11 duodenum, 10 saliva) that were missing the starting volumeinformation.

Poisson quality filtering: Two separate quality-filtering steps based onPoisson statistics were used to determine the statistical confidence inthe measured values. First, a 95% confidence interval was calculatedfrom the repeated measures of water blanks. Samples with a totalmicrobial load below the upper bound of this confidence interval wereremoved from further analysis.

Second, the limit of detection (LOD) in terms of relative abundance wasdetermined for each sample. Sequencing can be divided into two separatePoisson sampling steps. First, an aliquot of sample is taken from theextracted sample and input into the library amplification reaction. TheLOD of the library amplification step was determined by multiplying thetotal microbial load from dPCR by the input volume into the libraryamplification reaction and then finding the relative abundancecorresponding to an input of three copies. Poisson statistics tells usthat the likelihood of sampling one or more copies with an average inputof three copies is 95%. The second Poisson sampling step in sequencingarises from the number of reads generated from the amplified library.The accuracy of the second Poisson sampling step was previouslyshown[181] to follow a negative exponential curve, LOD=7.115*readdepth^(−0.115), between the total read depth and relative abundance atwhich 95% confidence of detection is observed. The minimum of the twodescribed LODs (first determined per sample by total load, and second bysequencing depth) was then determined for each sample. For each sample,the abundance of any ASV with a relative abundance below the LOD was setto zero. After filtering, data tables for each taxonomic level weregenerated.

Data transforms and dimensionality reduction: For PCA, all absolutetaxon abundances were log-transformed. To handle zeros, a pseudo-countof 0.1 reads was added to all taxon relative abundances beforemultiplying by each sample's total microbial load as determined bydigital PCR. PCA was performed with the sklearn.decomposition.PCAfunction in Python. Ranked feature loadings for each taxon on a givenprincipal component were determined by scaling the correspondingeigenvector by the maximum transformed value for that principalcomponent axis.

Statistical analysis and correlations: Group comparisons (e.g., SIBO vs.no SIBO, saliva vs. duodenum) were analyzed using the non-parametricKruskal-Wallis rank sums tests with Benjamini-Hochberg multiplehypothesis testing correction using SciPy.stats Kruskal function andstatsmodels.stats.multitest multipletests function with the fdr_bhoption.

Correlation coefficients were either Spearman or Pearson andcorresponding P-values for all correlations were determined withscipy.stats.spearmanr or scipy.stats.pearsonr functions. Multiplehypothesis testing was performed for each group of correlations (e.g.taxa co-correlations, cytokine correlations) separately using theBenjamini-Hochberg procedure.

Data Availability: Sequencing data generated during this study areavailable in the National Center for Biotechnology Information SequenceRead Archive repository under study accession number PRJNA674353. Rawdata for each figure and IPython notebooks for data processing andfigure generation are available through CaltechDATA:https://data.caltech.edu/records/1701.

Patient enrollment: the microbiome of the duodenum and its potentialrelationship with health and disease in a cohort of 250 patientsenrolled in the REIMAGINE study at Cedars-Sinai Medical Center werestudied. All patients undergoing esophagogastroduodenoscopy (EGD)without colonoscopy preparation as standard of care were eligible toenroll, resulting in patients with a wide range of GI conditions. Nohealthy controls are currently approved to be included in the study dueto the risks associated with the EGD procedure. Summary statistics forpatient demographic data and selected metadata categories from theenrollment questionnaire are included in Table 3.

Table 3 shows the summary statistics for the patient cohort used in thisstudy. All patients are from the REIMAGINE study.

TABLE 3 Total subjects 250 Duodenal Aspirate 250 Saliva 21 Mean (StdDev) Age 56.9 (14.9) Weight (lbs) 169 (50) Percent (N) Male FemaleGender 46% (115) 54% (135) Antibiotic usage last 6 months 40% (100)Current 4% (11) PPI usage PPI 34% (86) H2 blocker 4% (10) Both 4% (10)Any probiotic usage 20% (49) Smokers 6% (16) Bloating > 40 42% (106)Constipation > 40 30% (75) Incomplete Evac > 34% (84) 40 Excess Gas > 4044% (109) Diarrhea > 40 31% (77)

Example 21: A Log-Normal Distribution of the Total Microbial Load of theDuodenum Across Patients with GI Symptoms

In this example, the total microbial load from 250 human duodenalaspirates were examined using the recently developed digital PCRanchored 16S rRNA gene amplicon sequencing and quantification method.

The digital PCR-based determination of total microbial load[181, 182]from 250 human duodenal aspirates revealed samples that spanned loadsfrom the detection limit of ˜5×10³ rRNA gene copies/mL up to nearly 10⁹copies/mL. The overall distribution of total loads was log-normal withmean=6.13 Log₁₀ copies/mL and standard deviation=1.12 Log₁₀ copies/mL(FIG. 49, panels A, B). A bimodal distribution of total load indicativeof potential separation of patients into SIBO and non-SIBO diagnosis wasnot observed. Neither age nor gender significantly correlated with totalmicrobial load (FIG. 50). Total microbial load also did not correlatewith patient reported intake of probiotics supplements or yogurts,smoking, or usage of proton pump inhibitors (FIG. 51). Currentantibiotic usage appeared to lower the average total microbial load, butantibiotic usage in the previous six months had no impact (FIG. 51).

Digital PCR anchored 16S rRNA gene amplicon sequencing[181] providedabsolute taxon abundances in each sample and a statistical framework fordifferentiation between real and contaminant taxa (see Example 20). Theculture counts from aerobic (MacConkey agar) and anaerobic (blood agar)plates were first compared to the total load of microbes expected togrow on these plates (FIG. 52). For aerobic plating, a bimodaldistribution of combined Escherichia-Shigella, Enterobacteriaceae,Enterococcus, and Aeromonas bacterial load from quantitative sequencingand culture and a high correlation between the two measurements(Spearman, 0.61, P<0.001, N=244) was observed. For anaerobic plating,lower concordance (Spearman, 0.35, P<0.001, N=244) between quantitativesequencing and culture was observed. This discrepancy could reflect thedifficulty in culturing many intestinal microbes[186], especiallyanaerobes that are initially collected and processed in aerobicenvironments.

Next, the log-transformed absolute-abundance distributions were analyzedfor the most prevalent genera in the dataset (FIG. 49, panel C).Prevalence is defined as a taxon's frequency of occurrence in thedataset.

Streptococcus was present in all 250 samples and followed anapproximately log-normal distribution with a mean load that wasapproximately half an order of magnitude below that of the mean totalmicrobial load and an equal standard deviation. Other genera showedwide-ranging distributions that deviated from normality. For example,Porphyromonas appears bimodal with two local maxima whereas Haemophilusexhibits a long tail towards higher microbial loads. The 23 mostprevalent genera in this study are also commonly found in the oralmicrobiota[22]. A subset of these genera (Streptococcus, Veillonella,Fusobacterium, Prevotella 7, Prevotella) are also commonly found instool samples, indicating possible survival of these genera throughoutthe entire GI tract[23]. The majority of prevalent genera are eitherstrict or facultative anaerobes, indicating that parts of the duodenalenvironment are likely anoxic in this patient population.

Example 22: Direct Transmission of Microbes from Saliva to Duodenum

This example was carried out to investigate whether many of the taxafound in the duodenum originated from the oral cavity.

A subset of 21 patients was analyzed, for whom saliva and duodenumsamples collected during the same hospital visit were paired. DigitalPCR revealed that the total microbial load in saliva was roughly 2.5orders of magnitude higher than the total load in the duodenum(Kruskal-Wallis, P<0.001).

Further, the range in saliva total loads was 3 orders of magnitudesmaller than the range in total loads of the duodenum samples (FIG. 53,panel A). No significant correlation was observed between the totalmicrobial loads in paired saliva and duodenum samples (FIG. 53, panelB). In this study, all samples were taken with a custom double-sheathedcatheter via endoscope (see Methods) that moves beyond the outer sheathbefore aspirating duodenal fluid. This custom catheter should limit oralmicrobiota contamination of the duodenum during the procedure.Additionally, the optimized sample-processing protocol (see Methods)should eliminate extracellular DNA from swallowed dead bacteria.

To evaluate the direct transmission of microbes from saliva to duodenum,the shared taxa were compared between paired (same patient) and randomlypaired samples from the same dataset. On average, 89% (±6% S.D.) of thetaxa in the duodenum were also found in the paired saliva sample,whereas only 69% (±14% S.D.) were found when the pairs were randomized(FIG. 53, panel C, Kruskal-Wallis, P<0.001), suggesting directtransmission of oral taxa to the duodenum. Genera that wereproportionally enriched in either saliva or duodenum samples wereinvestigated. Campylobacter was present in 21/21 saliva samples but only10/21 duodenum samples. The absence of Campylobacter in about half ofthe paired duodenum samples could be the result of Campylobacter beingsensitive to the antibacterial properties of the stomach and smallintestine[187] (FIG. 53, panel D). In contrast, Streptococcus pneumoniaewas only found in duodenum samples (6/21) (FIG. 53, panel D). Thesedifferences in the relative abundance of specific taxa of microbesbetween paired saliva and duodenum samples also provide evidence againstoral contamination in the duodenal samples.

Example 23: Disruptor Taxa Revealed by Taxa Co-Correlations

In this example, the relationships between the top 20 most abundantgenera were analyzed. A co-correlation heatmap of these taxa revealedseveral distinct motifs (FIG. 54, panel A): (1) Taxa whose absoluteloads had a high correlation with total load, (2) taxa whose absoluteloads had a higher co-correlation with another taxon's absolute loadthan with total microbial load, (3) taxa with a mutually exclusiverelationship with almost all other abundant taxa. Correlation with totalload was often an indicator of a prevalent taxon because the variance intotal microbial load was larger than the variance in relative abundance.When two taxa have a higher co-correlation with each other than withtotal load it potentially indicates these taxa share preferredenvironmental factors or directly cooperate. One group of theseco-correlating taxa that included several Prevotella species and aspecies of Porphyromonas matches a known shared metabolic niche in theoral cavity[188, 189] (Table 4).

Table 4 shows two groups of taxa (in regular font and bold font) thathave stronger co-correlations with another taxon than with total load.Significance values for all correlations and co-correlations wereP<0.001.

TABLE 4 Co- Correlation with Biological Taxon 1 Taxon 2 CorrelationTotal Load Difference Link Alloprevotella Prevotella 0.73 0.30 0.43Tertiary plaque Prevotella 6 Prevotella 7 0.83 0.43 0.39 biofilmPorphyromonas Prevotella 0.74 0.36 0.38 colonizers. PrevotellaPrevotella 7 0.82 0.50 0.32 Metabolize same byproduct of primarycolonizers[188, 189].

0.69 0.43 0.26 Not Known

0.80 0.55 0.25

0.81 0.60 0.20

0.78 0.61 0.17

Several genera stood out as having no significant correlation withalmost all other abundant taxa: Enterobacteriaceae,Escherichia-Shigella, Clostridium sensu stricto 1, and Lactobacillus(FIG. 54, panel A). These taxa appeared to disrupt the commonly observedmicrobial structure (i.e., the prevalent taxa that generallyco-correlate with one another) of the duodenal microbiome. This patternof mutual exclusivity can be represented algorithmically by sorting alltaxa by the difference between their maximum abundance and their meanabundance. Practically, this means that these disruptors are relativelyrare (i.e., present in a small fraction of samples), but when they arepresent they usually dominate, excluding other common taxa.

A clustered heatmap of the top 16 taxa as ranked by the difference intheir maximum and mean abundances reveals two taxonomic signatures (FIG.54, panel B). The first signature in the top left of the heatmapcontained the mutually-exclusive taxa from the co-correlation heatmap,along with Enterococcus, Romboutsia, Aeromonas, and Bacteroides. Thesecond signature contained taxa that were generally found in lowerabundance, many of which are from the HACEK (Haemophilus,Aggregatibacter, Cardiobacterium, Eikenella, Kingella) group oforganisms associated with infective endocarditis[187]. However, thesecond group also clustered with more common taxa in this dataset, suchas Prevotella and Fusobacterium. Thus, all eight of the taxa in thefirst taxonomic signature were initially labelled as “disruptors” (FIG.54, panel B, bolded taxa) because their presence appeared to be mutuallyexclusive with many other common taxa.

Example 24: Aerobic Disruptor Taxa Displace Strict Anaerobes andDecrease Diversity

In this example, a principal component analysis (PCA) was performed onthe absolute taxon abundances to investigate the drivers of variance inthe dataset (FIG. 55, panel A).

Total loads spanned 5 orders of magnitude, accounting for most of thevariance. Total load cleanly separated samples along the PC1 axis. Thesecond most explanatory axis, PC2, strongly correlated with the Shannondiversity index of samples (Spearman, 0.74, P<0.001, N=250). Rankedfeature loadings for PC2 (FIG. 55, panel B) indicated that many of thedisruptor taxa (light grey) are the main drivers of separation in thepositive direction of PC2 whereas the five taxa driving most of theseparation in the negative direction (light grey) of PC2 consisted offour strict anaerobes (Porphyromonas, Leptotrichia, Prevotella,Prevotella 7) and one obligate aerobe (Neisseria). It should be notedthat many more taxa were strongly associated with the negative directionof PC2 than the positive direction. This separation matches well withthe mutual exclusivity seen between the disruptor taxa and otherorganisms in the co-correlation analysis.

The two disruptor taxa with the highest loads are aerobic pathogens fromthe Enterobacteriaceae family and the taxa most associated with thenegative direction of PC2 were strict anaerobes.

Next, the composition of strict vs facultative anaerobes in each samplewas investigated. It was found that a nearly 1:1 correlation between thestrict and facultative anaerobe loads across all samples (FIG. 55, panelC). Additionally, the fraction of strict anaerobes in a sample wasstrongly correlated (Pearson, 0.71, P<0.001, N=250) with Shannondiversity (FIG. 55, panel D), indicating that the disruptor taxa appearto be mutually exclusive with strict anaerobes and the “bloom” ofabsolute abundance of disruptors decreases Shannon diversity.

Example 25: Absolute Load of Disruptor Taxa Correlates with SIBO and GISymptoms

This example was carried out to determine whether disruptor taxa areassociated with disease or GI symptoms.

Applicant began by looking at patients with and without SIBO (SIBOclassification was made based on aerobic culture results, >103 CFU/mL ofduodenal aspirate[190]). Coloring the PCA plot by SIBO classificationindicates a clear enrichment of patients with SIBO in the positivedirection of the disruptor taxa axis (FIG. 56, panel A). Comparing theabsolute abundance of species between the SIBO and non-SIBO samples byKruskal-Wallis showed that the three most significantly different taxa(Enterobacteriaceae, Escherichia-Shigella, Clostridium perfringens) wereenriched in SIBO samples and were also the three most common disruptortaxa in all samples (FIG. 56, panel B).

Although one cannot be certain of the genera or species represented bythe Enterobacteriaceae classification, evidence from previous, publishedsequencing results from the REIMAGINE study using a different 16S primerset shows that many Enterobacteriaceae in duodenal samples from patientsin this study are Klebsiella species[191].

Lactobacillus abundance was similar in SIBO and non-SIBO samples (FIG.56, panel B) even though it co-correlated with many of the disruptortaxa (FIG. 54, panel B). Most of the non-SIBO samples that clusteredwith SIBO samples on the upper part of the PC plot containedLactobacillus (FIG. 56, panel B). Lactobacillus does not grow on theaerobic (MacConkey agar) plates used for SIBO classification, whichcould explain why these samples cluster together by sequencing but arenot classified as SIBO by culture.

Patient-reported GI symptom scores (on a 0-100 scale) were correlatedwith the disruptor taxa axis (PC2). Bloating, incomplete evacuation, andconstipation had the highest correlation with the disruptor taxa axis,whereas correlations between urgency, excess gas, or diarrhea and thedisruptor taxa axis were much weaker (FIG. 56, panel C). There was aweak positive correlation between the disruptor taxa axis and seruminterleukin 8 (IL8) levels (Spearman, 0.24, P<0.001, N=232), indicatinga potential neutrophil-related response (FIG. 56, panel D). However,none of the symptoms or cytokines had a significant correlation with thetotal load axis (PC1). C. perfringens was the single taxa that, whenpresent in patients, coincided with a significant increase(Kruskal-Wallis, P=0.039) in serum IL8 levels (FIG. 57). However, therewere only 9/250 samples with C. perfringens, limiting our ability todraw conclusions about this relationship.

In half of the samples containing the two most common disruptor taxa(Enterobacteriaceae, and Escherichia-Shigella), the total microbialloads were greater than 10⁷ rRNA gene copies/mL, indicating a clearenrichment of disruptor taxa in samples with higher than average totalmicrobial loads (FIG. 58). Lactobacillus did not follow this trend andwas found in samples with total microbial loads that were similar to thetotal loads of samples containing common taxa like Prevotella (FIG. 58).Additionally, in patients with high disruptor taxa loads (afterexcluding Lactobacillus load) the presence of Lactobacillus at greaterthan 5×10⁴ copies/mL negatively correlated with bloating symptoms (FIG.59).

These two facts and the abundance of literature on the potential healthbenefits of Lactobacillus[192, 193] led one to believe Lactobacilluslikely has a more nuanced relationship with the host than the other taxaclassified as disruptors. Thus, Lactobacillus was removed from the listof disruptor taxa in the analyses of the association of disruptors withtotal load (FIG. 56, panel E) and GI symptoms (FIG. 56, panel F). Whenmultiple disruptor taxa were present, there was a significant increasein total microbial load (Kruskal-Wallis, P<0.001; FIG. 56, panel E).

Patient-reported symptom scores are inherently qualitative, so to testwhether disruptor taxa loads were correlated with more severe GIsymptoms, the 0-100 scores were turned into a binary yes/no variable,representing a severe symptom, by drawing a threshold at the medianscore reported for each symptom (FIG. 60). The percentage of patientswith zero severe symptoms and the percentage of patients with manysevere symptoms (people reporting severe symptoms in 5-6 of the 6symptom categories) as a function of disruptor taxa loads were thencalculated. Three observations were made. First, at higher disruptorloads, patients were more likely to have more severe GI symptoms.Second, none of the patients with disruptor loads greater than 10⁷copies/mL (N=10) had zero symptoms whereas 60% of them had 5 or 6symptoms. Of the patients without disruptor taxa (N=153), 23% had zerosymptoms and 30% had 5 or 6 symptoms. Disruptor loads may also be higheras a function of age, all but one of the individuals with disruptorloads greater than 10⁶ copies/mL (N=23) were older than 50 (FIG. 61).The absolute and relative abundances of disruptor taxa did not correlate(FIG. 62), preventing the clear connection between abundant symptoms andhigh absolute loads of disruptor taxa from being observed when analyzingonly relative abundances.

Example 26: Exemplary Customized Rodent Models

In view of the above, an averaged microbiome profile can be providedwhich can be used to provide a rodent model of the common smallintestinal microbiome presence of streptococcus, Veillonella,fusobacterium, Prevotella, Neisseria where each has a relative abundanceof greater than 0.1% and the total load is roughly 1e6 copies/mL.

An averaged microbiome profile can also be provided which can be used toprovide a rodent model of human SIBO comprising presence ofEnterobacteriaceae with a relative abundance greater than 1% and thetotal load greater than 1e6 copies/mL.

An averaged microbiome profile can also be provided which can be used toprovide a rodent model of the common stool microbiome comprisingpresence of bacteroides, lachnospiraceae, ruminococcaceae where each hasa relative abundance of greater than 0.1% and the total load is roughly1e11 copies/mL.

In summary, provided herein are methods and systems and relatedcomposition, to provide a rodent model having a target microbiomeprofile formed by a target presence, a target proportion and/or a targettotal load of a target prokaryote of a target taxon, based on absolutequantification of the target prokaryote. Further provided are rodentsidentified and/obtained by the methods herein described and related usein testing methods performed in connection with physiological orpathological conditions in an individual preferably a human individual.

In particular, methods and systems and related composition, to provide arodent model having a target microbiome profile are performed usingmethods and systems for absolute quantification of a target 16S rRNAand/or of a target prokaryotic taxon, based on amplifying and sequencinga same 16S rRNA recognition segment in which target 16S rRNA conservedregions flank 16S rRNA variable regions, conserved and variable among aplurality of sample 16S rRNAs and/or of a sample prokaryotic taxon ofhigher taxonomic rank with respect to the target taxon. In the methodsand systems, absolute abundance of the a plurality of sample 16S rRNAsand/or of the sample prokaryotic taxon detected by the amplifying, ismultiplied by the relative abundance of the target 16S rRNA and/or of atarget prokaryotic taxon detected by the sequencing to provide theabsolute quantification in accordance with method and systems of thedisclosure.

Provided herein are also a tail-cup and related use to preventcoprophagia in rodent and/or other coprophagic animals. Further providedherein is a non-coprophagic rodent identified and/or obtained by thecombined used of the tail-cup and the absolute quantification methodsherein described and related use and methods identifiable by a skilledperson upon reading of the present disclosure.

The examples set forth above are provided to give those of ordinaryskill in the art a complete disclosure and description of how to makeand use the embodiments of the compounds, compositions, rodents, systemsand methods of the disclosure, and are not intended to limit the scopeof what the inventors regard as their disclosure. All patents andpublications mentioned in the specification are indicative of the levelsof skill of those skilled in the art to which the disclosure pertains.

The entire disclosure of each document cited (including webpagespatents, patent applications, journal articles, abstracts, laboratorymanuals, books, or other disclosures) in the Background, Summary,Detailed Description, and Examples is hereby incorporated herein byreference. All references cited in this disclosure, including referencescited in any one of the Appendices, are incorporated by reference to thesame extent as if each reference had been incorporated by reference inits entirety individually. However, if any inconsistency arises betweena cited reference and the present disclosure, the present disclosuretakes precedence. Further, the computer readable form of the sequencelisting of the ASCII text file P2506-USCIP-Sequence-Listing_ST25 isincorporated herein by reference in its entirety.

The terms and expressions which have been employed herein are used asterms of description and not of limitation, and there is no intention inthe use of such terms and expressions of excluding any equivalents ofthe features shown and described or portions thereof, but it isrecognized that various modifications are possible within the scope ofthe disclosure claimed. Thus, it should be understood that although thedisclosure has been specifically disclosed by embodiments, exemplaryembodiments and optional features, modification and variation of theconcepts herein disclosed can be resorted to by those skilled in theart, and that such modifications and variations are considered to bewithin the scope of this disclosure as defined by the appended claims.

It is also to be understood that the terminology used herein is for thepurpose of describing particular embodiments only, and is not intendedto be limiting. As used in this specification and the appended claims,the singular forms “a,” “an,” and “the” include plural referents unlessthe content clearly dictates otherwise. The term “plurality” includestwo or more referents unless the content clearly dictates otherwise.Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which the disclosure pertains.

When a Markush group or other grouping is used herein, all individualmembers of the group and all combinations and possible subcombinationsof the group are intended to be individually included in the disclosure.Every combination of components or materials described or exemplifiedherein can be used to practice the disclosure, unless otherwise stated.One of ordinary skill in the art will appreciate that methods, deviceelements, and materials other than those specifically exemplified may beemployed in the practice of the disclosure without resort to undueexperimentation. All art-known functional equivalents, of any suchmethods, device elements, and materials are intended to be included inthis disclosure. Whenever a range is given in the specification, forexample, a temperature range, a frequency range, a time range, or acomposition range, all intermediate ranges and all subranges, as wellas, all individual values included in the ranges given are intended tobe included in the disclosure. Any one or more individual members of arange or group disclosed herein may be excluded from a claim of thisdisclosure. The disclosure illustratively described herein suitably maybe practiced in the absence of any element or elements, limitation orlimitations which is not specifically disclosed herein.

A number of embodiments of the disclosure have been described. Thespecific embodiments provided herein are examples of useful embodimentsof the invention and it will be apparent to one skilled in the art thatthe disclosure can be carried out using a large number of variations ofthe devices, device components, methods steps set forth in the presentdescription. As will be obvious to one of skill in the art, methods anddevices useful for the present methods may include a large number ofoptional composition and processing elements and steps.

In particular, it will be understood that various modifications may bemade without departing from the spirit and scope of the presentdisclosure. Accordingly, other embodiments are within the scope of thefollowing claims.

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1. A method to provide a rodent model having a target microbiome profileformed by a target presence, a target proportion and/or a target totalload of a target prokaryote of a target taxon, the target taxon having ataxonomic rank lower than a sample taxon in a same taxonomic hierarchy,the method comprising providing a rodent having a rodent microbiome;obtaining a sample of the rodent comprising the rodent microbiome toprovide a rodent sample; quantifying absolute abundance of the targetprokaryotes in the rodent sample, to obtain a rodent detected presence,a rodent detected proportion and/or a rodent detected total load of thetarget prokaryotes in the rodent, each target prokaryote being of atarget taxon having a taxonomic rank lower than a sample taxon in a sametaxonomic hierarchy; comparing the rodent detected presence, the rodentdetected proportion of and/or the rodent detected total load of thetarget prokaryotes with the target presence, the target proportionand/or the target total load of the target prokaryotes in the targetmicrobiome profile, wherein quantifying in the modified sample absoluteabundance of the target prokaryotes is performed by amplifying a 16SrRNA recognition segment comprising a 16S rRNA variable region specificfor the target taxon flanked by target 16S rRNA conserved regionsspecific for the sample taxon, by performing amplification of nucleicacids extracted from the sample with primers comprising a primer targetsequence specific for the target 16S rRNA conserved regions toquantitatively detect an absolute abundance of prokaryotes of the sampletaxon in the sample and to provide an amplified 16S rRNA recognitionsegment, sequencing the amplified 16S rRNA recognition segment withprimers comprising the primer target sequence specific for the target16S rRNA conserved region and the 16S rRNA variable regions to detect arelative abundance of the prokaryotes of the target taxon with respectto the prokaryotes of the sample taxon in the sample, and multiplyingthe relative abundance of the prokaryotes of the target taxon in thesample times absolute abundance of the prokaryotes of the sample taxonin the sample to quantify the absolute abundance of the prokaryotes ofthe target taxon in the sample, and wherein the detected presence of thetarget prokaryote in the rodent is provided by the detected relativeabundance and/or absolute abundance wherein presence is detected is therelative abundance is greater than a threshold, the detected proportionis provide by the relative abundance of the target prokaryote and/or bythe quantified absolute abundance in combination with the detectedrelative abundance providing the detected proportion of the targetprokaryote in the rodent, and the total load of the target prokaryote isprovided by detected absolute abundance of the target prokaryote of thetarget taxa and/or the detected absolute abundance of prokaryotes of thesample taxon.
 2. The method of claim 1, further comprising modifying therodent microbiome by introducing, enriching and/or depleting prokaryotesin the rodent microbiome to provide the rodent microbiome with thetarget prokaryotes with the target presence, the target proportion,and/or the target total load; obtaining a sample of the rodentcomprising the rodent microbiome following the modifying to obtain arodent modified sample; quantifying absolute abundance of the targetprokaryotes in the rodent modified sample, to obtain a detected rodentmodified presence, a detected rodent modified proportion and/or adetected rodent modified total load of the target prokaryotes in therodent, each target prokaryote being of a target taxon having ataxonomic rank lower than a sample taxon in a same taxonomic hierarchy;and comparing the detected rodent modified presence, the detected rodentmodified proportion and/or the detected rodent modified total load ofthe target prokaryotes with the target presence, the target proportionand/or the target total load of the target prokaryotes in the targetmicrobiome profile.
 3. The method of claim 2 further comprisingrepeating the modifying, the obtaining to provide a rodent modifiedsample, the quantifying absolute abundance of the target prokaryotes inthe rodent modified sample, and the comparing, until the detected rodentmodified presence, the detected rodent modified proportion and/or thedetected rodent modified total load of the target prokaryote issubstantially the same of the target presence, the target proportionand/or the target total load of the target prokaryotes, to obtain therodent model having the target microbiome profile.
 4. The method ofclaim 2, wherein the modifying comprises preventing coprophagia of therodent.
 5. The method of claim 1, wherein the microbiome is a microbiomeof the gastrointestinal tract of a human individual.
 6. The method ofclaim 1, wherein the target microbiome is associated with aphysiological or pathological condition of a human individual.
 7. Themethod of claim 1, wherein the target prokaryote comprises aLactobacillus and Bifidobacterium strain.
 8. The method of claim 1,wherein the target microbiome is associated with a pathologicalcondition of a human individual and wherein, the target prokaryotecomprises at least one Proteobacteria prokaryotes selected fromEscherichia, Serratia, Campylobacter, Enterococcus, Klebsiella,Pseudomonas, Staphylococcus, Salmonella and Yersinia prokaryotes.
 9. Themethod of claim 1, wherein the target microbiome is associated with apathological condition of a human individual and wherein, the targetprokaryote comprises Clostridium difficile prokaryote.
 10. The method ofclaim 1, wherein the target microbiome is associated with a pathologicalcondition of the immune system and/or gastrointestinal tract of thehuman individual.
 11. The method of claim 1, wherein the target 16SrRNAs conserved regions have a homology of at least 90% among the 16SrRNAs of prokaryotes of the sample taxon.
 12. The method of claim 1,wherein the target 16S rRNAs conserved regions range from 15 to 25nucleotides.
 13. The method of claim 1, wherein the 16S rRNA variableregion comprises at least one region having a signature sequence uniqueto the 16S rRNAs of prokaryotes of the target taxon.
 14. The method ofclaim 1, wherein the primer target sequence has at least 90% homologywith the target 16S rRNAs conserved regions.
 15. The method of claim 1,wherein the 16S rRNA is a 16S rRNA gene.
 16. The method of claim 1,wherein the amplifying is performed by amplifying a first portion of theat least two portions of the sample to quantitatively detect an absoluteabundance of the plurality of sample 16S rRNAs in the sample, andamplifying a second portion of the sample to provide an amplified 16SrRNA recognition segment.
 17. The method of claim 16, wherein theamplifying the first portion of the sample is performed by digitalamplification of the 16S rRNA recognition segment, to quantitativelydetect an absolute abundance of the plurality of sample 16S rRNA in thesample and performing real-time PCR to provide an amplified 16S rRNArecognition segment.
 18. The method of claim 17, wherein the digitalamplification is performed by digital PCR.
 19. The method of claim 1,wherein the amplifying is performed by real-time qPCR.
 20. The method ofclaim 19, wherein the amplifying is performed with primers furthercomprising a barcode, adapter, linker, pad and/or frameshifting sequencefor next generation sequencing.
 21. The method of claim 20, wherein thesequencing and the amplifying are performed on a same sample or portionthereof to quantitatively detect an absolute abundance of the pluralityof sample 16S rRNA and to provide an amplified 16S rRNA recognitionsegment from the same sample or portion thereof.
 22. The method of claim19, wherein sequencing the amplified 16S rRNA recognition segment isperformed by amplicon sequencing.
 23. The method of claim 19, whereinthe sequencing is performed by next generation sequencing with primersfurther comprising a barcode, adapter, linker, pad and/or frameshiftingsequence.
 24. The method of claim 23, wherein the primers used insequencing the amplified 16S rRNA recognition segment further comprisean indexing primer for multiplexing/combining amplicons from multiplesamples for simultaneous next generation sequencing.
 25. The method ofclaim 19, wherein the primers comprise a forward primer comprising aprimer target sequence of SEQ ID NO: 25 and a reverse primer comprisinga primer target sequence of SEQ ID NO:
 26. 26. A system to provide arodent model having a target gut microbiome profile formed by a targetpresence, a target proportion and/or a target total load of a targetprokaryote of a target taxon, the system comprising a rodent, primerscomprising the target primer sequence specific for the target 16S rRNAconserved regions specific for the sample taxon, reagents to performpolymerase chain reaction, and reagents to perform amplicon sequencingfor simultaneous combined or sequential use to detect an absoluteabundance of target prokaryotes of the target taxon in the sampleaccording to the method of claim
 1. 27. The system of claim 26, whereinthe 16S rRNA recognition segment comprises one or more of 16Sr RNAvariable regions V1-V9 and the primers comprise a forward primer and areverse primer each comprising a primer target sequence specific targetconserved 16S rRNA region flanking the one or more of 16S rRNA variableregions V1-V9.
 28. The system of claim 26, wherein a forward primer ofthe primers comprises a primer target sequence of SEQ ID NO: 25 and areverse primer of the primers, comprises a primer target sequence of SEQID NO:
 26. 29. The system of claim 26, wherein the primers furthercomprise a barcode, adapter, linker, pad and/or frameshifting sequence.30. The system of claim 26, wherein the reagents to performamplification comprise reagents to perform digital PCR, digital LAMPand/or digital RPA.
 31. The system of claim 26, wherein the reagents toperform amplification comprise reagents to perform qPCR.
 32. The systemof claim 26, wherein the reagents to perform sequencing comprisereagents to perform next generation sequencing.
 33. The system of claim26, wherein the rodent is a rodent model selected from Germ-freerodents; Rodents pre-treated with antimicrobial agents, Gnotobioticrodents, Humanised rodents and chimeric rodents.